Power system power distribution network energy storage stability regulation and control method, medium and system

Abstract

The invention discloses an electric power system power distribution network energy storage stability regulation and control method, a medium and a system, and the method comprises the steps: carrying out the parameter feature extraction and analysis of a load demand, a power grid frequency, a power grid fault and a power factor of an electric power system, and obtaining different power supply group load fluctuation intervals of the charging and discharging power of an energy storage integrated management platform in unit time; generating a fault repair efficiency monitoring index of the energy storage integrated management platform, obtaining unit time jump ranges of active power and reactive power of the power system based on the leakage monitoring and stability regulation and control platform of the energy storage device of the power distribution network, and sending the unit time jump ranges and the fault repair efficiency monitoring index of the energy storage integrated management platform to a terminal of a power distribution network visual centralized control center; the power distribution network visualization centralized control center analyzes to obtain a stability regulation and control encryption coding signal, and issues a maintenance instruction for preventing leakage of the power distribution network energy storage device to the power system, so that the stability of the power distribution network energy storage device is greatly improved, and the safety of the power system is ensured.

Description

Technical Field

[0001] The present invention relates to the field of stability control of energy storage devices in distribution networks, and in particular to a stability control method, medium and system for energy storage in distribution networks of power systems. Background Art

[0002] The existing power system distribution network energy storage control technology still has some shortcomings in practice. First, the traditional method is relatively rough in analyzing the load demand of the power grid, and it is difficult to accurately predict the dynamic load changes, which leads to the lag and inefficiency of the control strategy of the energy storage system in actual operation. In addition, the existing technology is not good at fault monitoring and response. Most monitoring systems can only perform passive processing after the fault occurs, lacking early warning and active intervention capabilities, which can easily lead to the spread of local problems in the power grid and cause large-scale instability or even power outage risks.

[0003] In terms of the coordinated control of energy storage systems and power grids, existing technologies usually ignore the differentiated needs of different power supply areas and user groups and adopt a unified control scheme. This "one-size-fits-all" approach cannot fully utilize the resource potential of the energy storage system, especially in areas where load fluctuations are obvious or peak currents occur frequently. The control efficiency is low and it is difficult to achieve optimal resource allocation. In addition, existing methods lack effective monitoring and management methods for the operating health status of energy storage devices (such as battery energy storage, mechanical energy storage, etc.), and cannot dynamically adjust the charging and discharging power of energy storage equipment, which can easily cause equipment overload or premature aging, affecting the overall performance and service life of the energy storage system.

[0004] The existing technology also has the problem of insufficient data interaction between the power grid monitoring platform and the energy storage management system. Due to the lack of a unified integrated management platform, the information island phenomenon between the various subsystems is serious, and the global scheduling and optimization analysis of the energy storage device cannot be achieved. At the same time, most of the existing control strategies are based on static model design, ignoring the real-time changes in the power grid load and the operating status of the energy storage equipment, resulting in insufficient dynamic adjustment capabilities. In addition, when dealing with complex fault scenarios (such as instantaneous fluctuations caused by spike currents), the energy storage system has a slow response speed and insufficient accuracy, making it difficult to provide sufficient stability support under high-volatility operating conditions of the power grid. Therefore, the existing technology still has a lot of room for improvement in terms of dynamics, accuracy, and intelligence. It is necessary to make up for these shortcomings through innovative stability control methods to provide a more comprehensive and efficient solution for the safe operation of the distribution network. Summary of the invention

[0005] The present application provides a stability control method, medium and system for energy storage in a distribution network of an electric power system, which is used to solve the technical problem in the prior art that there is insufficient control over the range of jumps in active power and reactive power per unit time, resulting in inaccurate assessment of real-time peak current anomalies and inability to accurately manage leakage of energy storage devices in distribution networks.

[0006] In view of the above problems, the present application provides a stability control method, medium and system for energy storage in a power distribution network of a power system.

[0007] In a first aspect, the present application provides a method for regulating the stability of energy storage in a power distribution network of a power system, the method comprising:

[0008] Step S100, by extracting and analyzing parameter features of load demand, grid frequency, grid fault and power factor of the power system, the load fluctuation range of different power supply groups of the energy storage integrated management platform for charging and discharging power per unit time is obtained;

[0009] Step S200, generating a fault repair efficiency monitoring index for the energy storage integrated management platform through the load fluctuation range of different power supply groups of the charging and discharging power per unit time of the energy storage integrated management platform, and using the leakage monitoring and stability control platform of the distribution network energy storage device to obtain the jump range of the active power and reactive power per unit time of the power system;

[0010] Step S300: sending the active power and reactive power jump range per unit time and the energy storage integrated management platform fault repair efficiency monitoring index to the terminal of the distribution network visualization centralized control center, using the distribution network visualization centralized control center to analyze and obtain a stability control encrypted coding signal, and using the stability control encrypted coding signal to generate different stability control load fluctuation ranges;

[0011] Step S400, inputting the different stability control load fluctuation intervals into the stability control analysis algorithm for preventing leakage of distribution network energy storage devices of the chemical, mechanical and thermal energy storage integrated management platform for different power supply groups, and obtaining leakage cause nodes of different distribution network energy storage devices according to the stability control analysis algorithm for preventing leakage of distribution network energy storage devices of the chemical, mechanical and thermal energy storage integrated management platform for different power supply groups, wherein the leakage cause nodes of different distribution network energy storage devices correspond to the different stability control load fluctuation intervals;

[0012] Step S500: issuing a maintenance instruction to prevent the distribution network energy storage device from leaking to the power system through the different distribution network energy storage device leakage cause nodes.

[0013] In a second aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, and when the program instructions are executed, they are used to execute a stability control method for energy storage in a power system distribution network.

[0014] In a third aspect, the present invention provides a stability control system for energy storage in a power system distribution network, comprising a processor and a computer-readable storage medium.

[0015] Beneficial effect: The present application provides a stability control method, medium and system for energy storage in the distribution network of a power system, which relates to the technical field of leakage monitoring and stability control of energy storage devices in distribution networks. It solves the shortcomings of the prior art in obtaining the jump range of active power and reactive power per unit time, as well as the incomplete node analysis of the cause of leakage of energy storage devices in distribution networks. It proposes a stability control analysis algorithm for preventing leakage of energy storage devices in distribution networks for a comprehensive management platform for chemical, mechanical and thermal energy storage of different power supply groups. The algorithm includes a current frequency per unit time prediction model, a battery internal resistance tolerance response model and a distribution network energy storage device leakage level prediction model. The proposed model not only predicts the leakage of energy storage devices in distribution networks, but also predicts the leakage level of energy storage devices in distribution networks. It is used for monitoring, and also for supervising the leakage of distribution network energy storage devices caused by battery internal resistance and human factors. Different load fluctuation intervals are used to supervise the leakage of distribution network energy storage devices in many aspects. After the system analyzes, if there is a risk of leakage of distribution network energy storage devices, the system uses the maintenance instruction module to prevent the leakage of distribution network energy storage devices to respond quickly, and notifies the management personnel of the energy storage integrated management platform to carry out rapid maintenance instructions, which effectively prevents the occurrence of leakage of distribution network energy storage devices. At the same time, the present invention adopts the distribution network visualization centralized control center for data analysis, and the calculation speed is fast, which greatly improves the practicality and effectiveness of the proposed system and the accuracy of the calculation of the leakage of distribution network energy storage devices. The present invention generates a control strategy that adapts to different power supply conditions by accurately analyzing the power load demand and the fluctuation characteristics of the power grid, which can effectively cope with the complex and changeable power grid operation environment and improve the adaptability and flexibility of the system operation. Secondly, the method uses the energy storage integrated management platform and the stability control platform to work together to monitor and control the jump range of active power and reactive power in real time, ensuring the smooth operation of the power system under peak load conditions, thereby improving the reliability of the power system. The fault repair efficiency monitoring index and visual analysis function mentioned in the present invention not only facilitate the grasp of the power grid status, but also can quickly identify potential fault nodes, improve operation and maintenance efficiency, and reduce the impact of equipment failures on system stability. At the same time, the method refines the control measures through classified and zoned analysis, especially for the accurate evaluation of group chemical, mechanical and thermal energy storage characteristics, so that it has higher adaptability and control accuracy in different scenarios. The present invention strengthens the steady-state operation of the power system by preventing potential problems such as leakage of energy storage equipment, and further improves the safety and long-term operation capability of the system. Overall, the method can achieve efficient scheduling and precise control of power system energy storage, and provide strong technical support for the stable operation of the distribution network and the optimization of the energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a flow chart of the method of the present invention;

[0017] Figure 2 This is a diagram of the system module structure of the present invention. DETAILED DESCRIPTION

[0018] The present application provides a stability control method, medium and system for energy storage in a power distribution network of a power system, so as to solve the technical problem that the prior art is insufficient in controlling the range of jumps in active power and reactive power per unit time, resulting in inaccurate assessment of real-time peak current anomalies and inability to accurately manage leakage of energy storage devices in distribution networks.

[0019] like Figure 1 As shown, an embodiment of the present application provides a stability control method for energy storage in a power system distribution network, which is applied to a leakage monitoring and stability control platform for energy storage devices in a distribution network. The leakage monitoring and stability control platform for energy storage devices in a distribution network is connected to a distribution network visualization centralized control center via a 5G signal. The system includes:

[0020] Step S100: extracting and analyzing parameter features of load demand, grid frequency, grid fault and power factor of the power system to obtain load fluctuation ranges of different power supply groups of the energy storage integrated management platform for charging and discharging power per unit time;

[0021] A stability control method, medium and system for energy storage in a distribution network of a power system provided in an embodiment of the present application are applied to a leakage monitoring and stability control platform for energy storage devices in a distribution network. The leakage monitoring and stability control platform for energy storage devices in a distribution network is connected to a distribution network visualization control center via a 5G signal. The distribution network visualization control center is used to perform parameter analysis and calculation of the fault repair efficiency, active power and reactive power of the energy storage comprehensive management platform.

[0022] First, the power system is delineated, and parameter features of load demand, grid frequency, grid faults and power factor are extracted and analyzed in the delineated power system. The analysis of peak current anomalies refers to the all-weather, continuous and automatic monitoring of the geographical location, topography and terrain of the urban area in the environment, the frequency and number of natural leakage, the average current of the charging and discharging power per unit time, the location and discharge depth of historical peak current anomalies, the impact of human activities, the maximum load value of the battery internal resistance, the aging degree of the battery internal resistance and other data. Furthermore, the corresponding load fluctuation range of the charging and discharging power per unit time of the energy storage integrated management platform is obtained, which serves as an important reference for the later implementation of maintenance instructions for anti-leakage of distribution network energy storage devices.

[0023] Step S200: Generate a fault repair efficiency monitoring index for the energy storage integrated management platform by using the load fluctuation range of different power supply groups of the charging and discharging power per unit time of the energy storage integrated management platform, and obtain the jump range of active power and reactive power per unit time of the power system by using the leakage monitoring and stability control platform of the distribution network energy storage device;

[0024] On the basis of the load fluctuation range of different power supply groups of the charging and discharging power per unit time of the energy storage comprehensive management platform obtained above, the determined energy storage comprehensive management platform is subjected to the energy storage comprehensive management platform fault repair efficiency monitoring index corresponding to the load fluctuation range of different power supply groups of the charging and discharging power per unit time of the energy storage comprehensive management platform. When the index for monitoring the fault repair efficiency of the energy storage comprehensive management platform is generated based on the load fluctuation range of different power supply groups of the charging and discharging power per unit time of the energy storage comprehensive management platform, the relevant indexes for monitoring the fault repair efficiency of the energy storage comprehensive management platform may be: the caliber of the battery internal resistance, the number and area of ​​the low-lying areas on the ground, the efficiency of ground and underground discharge, the blockage distribution data of the battery internal resistance, the rationality data of the distribution of the battery internal resistance, the number of discharge trenches, etc., thereby providing a guarantee for implementing the maintenance instructions for preventing the leakage of the distribution network energy storage device.

[0025] Through the distribution network energy storage device leakage monitoring and stability control platform, the unit time jump range of the active power and reactive power of the power system will be obtained. The distribution network energy storage device leakage monitoring and stability control platform is based on the intelligent active power and reactive power technology of the distribution network energy storage device leakage monitoring and stability control automation. It will provide a scientific basis for the monitoring of the leakage monitoring and stability control mode of the distribution network energy storage device under the cooperation of intelligent theoretical knowledge and technology, and promote its stability control mode in future practice. The effect is more significant, meeting the requirements for efficient development of the distribution network energy storage device leakage monitoring and stability control work, and laying the foundation for the improvement of its stability control automation level. At the same time, in the Internet era Through in-depth analysis of the application of intelligent active power and reactive power technologies, effective research on leakage monitoring and stability control mode monitoring of distribution network energy storage devices can be carried out, which can provide technical support for its monitoring process, and continuously monitor the stability control mode during the operation of the energy storage integrated management platform for fault repair efficiency, and meet the requirements of the scientific development of its stability control automation. The active power and reactive power unit time jump range of the power system obtained based on the leakage monitoring and stability control platform of the distribution network energy storage device includes active power, reactive power real-time capacity, load data, active power, reactive power topology and geographic location data, which further lays a solid foundation for the subsequent realization of maintenance instructions to prevent leakage of distribution network energy storage devices.

[0026] Step S300: sending the active power and reactive power unit time jump range and the energy storage integrated management platform fault repair efficiency monitoring index to the terminal of the distribution network visualization centralized control center, using the distribution network visualization centralized control center to analyze and obtain a stability control encrypted coding signal, and using the stability control encrypted coding signal to generate different stability control load fluctuation ranges;

[0027] The active power and reactive power unit time jump range of the power system obtained by the distribution network energy storage device leakage monitoring and stability control platform and the energy storage comprehensive management platform fault repair efficiency monitoring index obtained according to the load fluctuation range of different power supply groups of the energy storage comprehensive management platform per unit time charging and discharging power are sent together to the terminal of the distribution network visualization control center. The distribution network visualization control center terminal is based on a reliable and high-speed specific communication protocol and utilizes high-speed data computing capabilities. It implements the management solution of customer terminal equipment through remote virtualization technology, and forms a host-client dynamic architecture by separating the remote terminal equipment hardware, running software and hard disk data into different transmission levels. Furthermore, on the basis of the distribution network visualization control center, the obtained active power and reactive power unit time jump range and the energy storage comprehensive management platform fault repair efficiency monitoring index are analyzed to obtain the stability control encrypted coding signal, which has a promoting effect on the implementation of maintenance instructions to prevent the leakage of distribution network energy storage devices.

[0028] After the stability control encrypted coded signal is obtained by analyzing the active power and reactive power unit time jump range and the energy storage integrated management platform fault repair efficiency monitoring index in the distribution network visualization centralized control center, different stability control load fluctuation ranges will be generated based on the stability control encrypted coded signal, where the stability control load fluctuation range is based on the active power and reactive power unit time jump range of the power system, the real-time capacity of active power and reactive power, load data, active power, reactive power topology and geographical location data, as well as the caliber of the city battery internal resistance in the energy storage integrated management platform fault repair efficiency monitoring index, ground low The number and area of ​​depressions, the efficiency of ground and underground discharge, the blockage distribution data of battery internal resistance, the distribution rationality data of battery internal resistance, the number of discharge trenches, etc., are based on the different active power and reactive power unit time jump ranges obtained in the power system and the fault repair efficiency monitoring indicators of different energy storage integrated management platforms to generate different stability control load fluctuation ranges, and the different stability control load fluctuation ranges generated in the power system are integrated to generate different stability control load fluctuation ranges, and the corresponding power system is monitored for corresponding stability control, which has a far-reaching impact on the later implementation of maintenance instructions to prevent leakage of distribution network energy storage devices.

[0029] Step S400: inputting the different stability control load fluctuation intervals into the stability control analysis algorithm for preventing the leakage of distribution network energy storage devices on the chemical, mechanical and thermal energy storage integrated management platform for different power supply groups, and obtaining the leakage cause nodes of different distribution network energy storage devices according to the stability control analysis algorithm for preventing the leakage of distribution network energy storage devices on the chemical, mechanical and thermal energy storage integrated management platform for different power supply groups, wherein the leakage cause nodes of different distribution network energy storage devices correspond to the different stability control load fluctuation intervals;

[0030] On the basis of the different stability control load fluctuation ranges obtained above, they are input into the stability control analysis algorithm for preventing leakage of distribution network energy storage devices on the chemical, mechanical and thermal energy storage comprehensive management platform for different power supply groups, wherein the stability control analysis algorithm for preventing leakage of distribution network energy storage devices on the chemical, mechanical and thermal energy storage comprehensive management platform for different power supply groups is a reinforcement learning model in machine learning that can continuously perform self-iterative monitoring, and the stability control analysis algorithm for preventing leakage of distribution network energy storage devices on the chemical, mechanical and thermal energy storage comprehensive management platform for different power supply groups is obtained through training with training data sets and supervision data sets, wherein each group of training data in the training data set includes different stability control load fluctuation range data; the supervision data set is the supervision data of the leakage cause nodes of different distribution network energy storage devices corresponding to the training data set.

[0031] Furthermore, the process of constructing the stability control analysis algorithm for preventing leakage of distribution network energy storage devices on the chemical, mechanical and thermal energy storage comprehensive management platform for different power supply groups is as follows: each group of training data in the training data set is input into the stability control analysis algorithm for preventing leakage of distribution network energy storage devices on the chemical, mechanical and thermal energy storage comprehensive management platform for different power supply groups, and the output supervision and adjustment of the stability control analysis algorithm for preventing leakage of distribution network energy storage devices on the chemical, mechanical and thermal energy storage comprehensive management platform for different power supply groups is performed through the supervision data corresponding to this group of training data. When the output result of the stability control analysis algorithm for preventing leakage of distribution network energy storage devices on the chemical, mechanical and thermal energy storage comprehensive management platform for different power supply groups is consistent with the supervision data, the training of the current group is completed, and the training of all the training data in the training data set is completed, and the training of the stability control analysis algorithm for preventing leakage of distribution network energy storage devices on the chemical, mechanical and thermal energy storage comprehensive management platform for different power supply groups is completed.

[0032] In order to ensure the accuracy of the stability control analysis algorithm of the chemical, mechanical and thermal energy storage integrated management platform for different power supply groups to prevent leakage of distribution network energy storage devices, the stability control analysis algorithm of the chemical, mechanical and thermal energy storage integrated management platform for different power supply groups to prevent leakage of distribution network energy storage devices can be tested through a test data set. For example, the test accuracy can be set to ninety percent. When the test accuracy of the test data set meets ninety percent, the stability control analysis algorithm of the chemical, mechanical and thermal energy storage integrated management platform for different power supply groups to prevent leakage of distribution network energy storage devices is completed.

[0033] The data of load fluctuation intervals under different stability control are input into the stability control analysis algorithm for preventing leakage of distribution network energy storage devices of the comprehensive management platform for chemical, mechanical and thermal energy storage of different power supply groups, and the leakage cause nodes of different distribution network energy storage devices are output. The leakage cause nodes of different distribution network energy storage devices obtained correspond to the load fluctuation intervals under different stability control. The management of the measures for preventing leakage of distribution network energy storage devices in the later stage can be better realized according to the leakage cause nodes of different distribution network energy storage devices obtained.

[0034] Step S500: issuing a maintenance instruction for preventing the energy storage device from leaking in the distribution network to the power system through the different distribution network energy storage device leakage cause nodes.

[0035] After inputting the above-mentioned different stability control load fluctuation ranges into the stability control analysis algorithm for preventing distribution network energy storage device leakage in the comprehensive management platform for chemical, mechanical and thermal energy storage of different power supply groups, the leakage cause nodes of different distribution network energy storage devices are obtained, and maintenance instructions for preventing distribution network energy storage device leakage are issued to the corresponding power system. The maintenance instructions for preventing distribution network energy storage device leakage are based on fast, effective and low risk. The core is to take effective measures as much as possible before large-scale distribution network energy storage devices leak, so as to minimize the risk of distribution network energy storage device leakage.

[0036] Furthermore, the present invention provides a stability control method, medium and system for energy storage in a distribution network of an electric power system, and relates to the technical field of leakage monitoring and stability control of energy storage devices in a distribution network. The system comprises: extracting and analyzing parameter features of load demand, grid frequency, grid fault and power factor of the electric power system, obtaining load fluctuation ranges of different power supply groups of the energy storage integrated management platform for charging and discharging power per unit time, generating fault repair efficiency monitoring indicators of the energy storage integrated management platform, obtaining the jump range of active power and reactive power per unit time of the electric power system based on the leakage monitoring and stability control platform of the energy storage device in the distribution network, and sending it to the terminal of the distribution network visualization centralized control center together with the fault repair efficiency monitoring indicators of the energy storage integrated management platform, and the distribution network visualization centralized control center performs analysis to obtain a stability control encrypted coding signal, and generates Different stability control load fluctuation intervals are input into the stability control analysis algorithm for preventing leakage of distribution network energy storage devices on the comprehensive management platform of chemical, mechanical and thermal energy storage for different power supply groups. According to the stability control analysis algorithm for preventing leakage of distribution network energy storage devices on the comprehensive management platform of chemical, mechanical and thermal energy storage for different power supply groups, the leakage cause nodes of different distribution network energy storage devices are obtained, and maintenance instructions for preventing leakage of distribution network energy storage devices are issued to the power system. The present invention solves the technical problem that the prior art is insufficient in controlling the jump range of active power and reactive power per unit time, which ultimately makes the real-time peak current anomaly assessment inaccurate, resulting in the inability to accurately manage the leakage of distribution network energy storage devices. The present invention realizes real-time monitoring of leakage of distribution network energy storage devices, and manages the leakage of distribution network energy storage devices for the power system according to the monitoring results.

[0037] Input the different stability control load fluctuation intervals into the stability control analysis algorithm for preventing leakage of distribution network energy storage devices of the different power supply groups' chemical, mechanical, and thermal energy storage integrated management platform, wherein the stability control analysis algorithm for preventing leakage of distribution network energy storage devices of the different power supply groups' chemical, mechanical, and thermal energy storage integrated management platform includes different algorithms, and the different algorithms include a current frequency unit time prediction model, a battery internal resistance withstand response model, and a distribution network energy storage device leakage level prediction model;

[0038] According to the stability control analysis algorithm of the chemical, mechanical and thermal energy storage integrated management platform for preventing leakage of distribution network energy storage devices of different power supply groups, the power system is analyzed for the leakage cause nodes of the distribution network energy storage devices of the peak current anomaly-energy storage integrated management platform fault repair efficiency, and the leakage cause nodes of the different distribution network energy storage devices are output, wherein each distribution network energy storage device leakage cause node corresponds to the maintenance instruction for preventing leakage of the distribution network energy storage device generated by each stability control load fluctuation interval.

[0039] Specifically, different stability control load fluctuation intervals generated based on the stability control encrypted coding signal are input into the stability control analysis algorithm for preventing leakage of distribution network energy storage devices on the chemical, mechanical and thermal energy storage comprehensive management platform for different power supply groups. The stability control analysis algorithm for preventing leakage of distribution network energy storage devices on the chemical, mechanical and thermal energy storage comprehensive management platform for different power supply groups includes different algorithms, which include a current frequency unit time prediction model, a battery internal resistance tolerance response model and a distribution network energy storage device leakage level prediction model. The current frequency unit time prediction model refers to the corresponding feedback of the real-time execution data of the stability control load fluctuation interval after receiving the stability control load fluctuation interval, and the battery internal resistance tolerance response model refers to the corresponding feedback of the data fed back in the current frequency unit time prediction model for the distribution network energy storage device. The leakage data comparison, the distribution network energy storage device leakage level prediction model refers to analyzing the data compared by the battery internal resistance withstand response model, and finally obtaining the maintenance instructions for the distribution network energy storage device leakage corresponding to the stability control load fluctuation range. On the basis of this process, the power system is subjected to peak current anomaly-energy storage comprehensive management platform fault repair efficiency distribution network energy storage device leakage cause node through different power supply groups chemical, mechanical, thermal energy storage comprehensive management platform to prevent the distribution network energy storage device leakage stability control analysis algorithm for analysis, and further output the corresponding different distribution network energy storage device leakage cause nodes, and each distribution network energy storage device leakage cause node corresponds to each stability control load fluctuation range generated by the distribution network energy storage device leakage maintenance instructions, to achieve the technical effect of providing an important basis for the later realization of the distribution network energy storage device leakage maintenance instructions.

[0040] Input the load fluctuation intervals of different stability control into the current frequency unit time prediction model, perform stability control of different power supply groups, and obtain unit time data matrices of different power supply groups, including data of different power supply groups before and after the peak current anomaly of the energy storage integrated management platform;

[0041] According to the battery internal resistance tolerance response model, the data of different power supply groups before the peak current abnormality of the energy storage integrated management platform and the data of different power supply groups after the peak current abnormality of the energy storage integrated management platform are compared to evaluate the battery internal resistance load operation data before and after the peak current abnormality;

[0042] According to the distribution network energy storage device leakage level prediction model, the peak current anomaly impact level analysis is performed on the battery internal resistance load operation data before and after the peak current anomaly, and the leakage cause nodes of the different distribution network energy storage devices are output.

[0043] Specifically, after inputting the obtained different stability control load fluctuation ranges into the stability control analysis algorithm of the chemical, mechanical, and thermal energy storage comprehensive management platform for preventing distribution network energy storage devices from leaking, the current frequency unit time prediction model is first entered, and the corresponding stability control is performed on the active power and reactive power, so as to obtain the unit time data matrix of different power supply groups, wherein the unit time data matrix of different power supply groups includes the data of different power supply groups before the energy storage comprehensive management platform spike current anomaly and the data of different power supply groups after the energy storage comprehensive management platform spike current anomaly. The obtained data then enters the battery internal resistance withstand response model, and the different power supply group data before the energy storage comprehensive management platform spike current anomaly and the different power supply group data after the energy storage comprehensive management platform spike current anomaly in the obtained unit time data matrix of different power supply groups are compared. The battery internal resistance load operation data before and after the peak current anomaly is compared with the power group data to further obtain the battery internal resistance load operation data before and after the peak current anomaly, wherein the battery internal resistance load operation data before and after the peak current anomaly can be the different power supply group data before the peak current anomaly of the energy storage integrated management platform minus the different power supply group data after the peak current anomaly of the energy storage integrated management platform, so that finally the distribution network energy storage device leakage level prediction model issues a maintenance instruction for preventing the leakage of the distribution network energy storage device for the obtained battery internal resistance load operation data before and after the peak current anomaly, wherein the maintenance instruction for preventing the leakage of the distribution network energy storage device refers to the ratio of the number of transformation behaviors of the anti-leakage measures of the distribution network energy storage device to the number of times in the total operation data within a statistical period, and outputs the leakage cause nodes of different distribution network energy storage devices to ensure the efficiency of the maintenance instruction for preventing the leakage of the distribution network energy storage device.

[0044] According to the leakage cause nodes of different distribution network energy storage devices, the energy storage integrated management platform performs a collaborative analysis of energy storage and smart grid to obtain a collaborative coefficient between energy storage and smart grid;

[0045] Determine whether the synergy coefficient between the energy storage and the smart grid exceeds the preset synergy coefficient between the energy storage and the smart grid, and when the synergy coefficient between the energy storage and the smart grid exceeds the preset synergy coefficient between the energy storage and the smart grid, obtain a leakage impact assessment coefficient;

[0046] Feedback calculation is performed on the leakage cause nodes of the different distribution network energy storage devices using the leakage impact assessment coefficient to obtain the impact weights of the leakage cause nodes of the different distribution network energy storage devices.

[0047] The coordination between the energy storage of the energy storage integrated management platform and the smart grid is analyzed based on the leakage cause nodes of different distribution network energy storage devices obtained above, and the coordination coefficient between the energy storage and the smart grid is obtained. The coordinated analysis between the energy storage of the energy storage integrated management platform and the smart grid refers to the coordination between the energy storage of the energy storage integrated management platform and the smart grid is determined by statistics on the historical discharge depth and the coordinated evaluation results of the energy storage and the smart grid are obtained after calculation using the convolutional neural network algorithm, and the coordination coefficient between the energy storage and the smart grid is obtained at the same time.

[0048] Further, the obtained synergy coefficient of the energy storage and the smart grid is judged to determine whether the obtained synergy coefficient of the energy storage and the smart grid exceeds the preset synergy coefficient of the energy storage and the smart grid, wherein the obtained preset synergy coefficient of the energy storage and the smart grid is preset by relevant technical personnel based on a large amount of distribution network energy storage device leakage conversion data, and finally the obtained synergy coefficient of the energy storage and the smart grid is compared with the preset synergy coefficient of the energy storage and the smart grid. If the synergy coefficient of the energy storage and the smart grid exceeds the obtained preset synergy coefficient of the energy storage and the smart grid, the leakage impact assessment coefficient is obtained, and the leakage impact assessment coefficient is the weight distribution of the mean value in the leakage conversion process of the distribution network energy storage device, and at the same time, the obtained leakage impact assessment coefficient is feedback calculated for the leakage cause nodes of different distribution network energy storage devices, that is, the mean weight is reconfigured according to the leakage cause nodes of different distribution network energy storage devices, so as to obtain the influence weights of the leakage cause nodes of different distribution network energy storage devices, and finally achieve the technical effect of providing a reference for the maintenance instruction of the distribution network energy storage device leakage prevention.

[0049] When the synergy coefficient between the energy storage and the smart grid is lower than the preset synergy coefficient between the energy storage and the smart grid, obtaining a key supervision signal;

[0050] The stability control load fluctuation interval of the synergy coefficient of the energy storage and the smart grid is labeled according to the key supervision signal, the labeled stability control load fluctuation interval is obtained, and the labeled stability control load fluctuation interval is focused on per unit time.

[0051] Specifically, the obtained synergy coefficient of the energy storage and the smart grid is compared with the preset synergy coefficient of the energy storage and the smart grid. If the synergy coefficient of the energy storage and the smart grid is less than the preset synergy coefficient of the energy storage and the smart grid, a key supervision signal is generated, wherein the key supervision signal is to label the stability control load fluctuation interval of the synergy coefficient of the obtained energy storage and the smart grid, so as to obtain the corresponding labeled stability control load fluctuation interval, and further, the power system in the labeled stability control load fluctuation interval is focused on per unit time, and finally the technical effect of preventing the distribution network energy storage device from leaking maintenance instructions is achieved.

[0052] collecting discharge depth information of the power system;

[0053] The discharge depths at different locations in the discharge depth information are converted to corresponding peak current abnormal distribution positions to generate peak current abnormal transmission line data;

[0054] Performing peak current anomaly evaluation on preset line nodes in the peak current abnormality transmission line data to obtain the working efficiency of the inverter with peak current abnormality;

[0055] Based on the peak current abnormal inverter working efficiency and the leakage cause nodes of the different distribution network energy storage devices, a maintenance instruction for preventing the distribution network energy storage device from leaking is issued to the power system.

[0056] Specifically, the discharge depth information in the power system is first determined, and based on the determined discharge depth information, the discharge depths of different locations in the discharge depth information are distributed to the corresponding peak current anomaly distribution positions, thereby generating peak current abnormality transmission line data, and further performing peak current anomaly evaluation on the preset line nodes in the obtained peak current abnormality transmission line data, and then obtaining the peak current abnormality inverter working efficiency corresponding to each position, and finally issuing a maintenance instruction to prevent the leakage of distribution network energy storage devices to the power system based on the obtained peak current abnormality inverter working efficiency and the leakage cause nodes of different distribution network energy storage devices.

[0057] Line voltage fluctuations, Objective: Evaluate the voltage fluctuations that may be caused by abnormal spike currents and their impact on system stability. Measures: Monitor voltage fluctuations, especially when spike currents occur at preset line nodes, and check whether the voltage exceeds the normal range. If the voltage fluctuation is too large, it may need to be adjusted by inverters or other power equipment.

[0058] Current and power factor analysis, Objective: Analyze the power factor fluctuation caused by the peak current to evaluate the operating efficiency of the power system. Measures: Calculate and analyze the power factor change, especially during the period of peak current, whether the power factor is lower than the standard value. The inverter may need to adjust the reactive power output to optimize the power factor and improve system efficiency.

[0059] Inverter efficiency evaluation, goal: to evaluate the efficiency of the inverter under abnormal peak current conditions. Measures: The efficiency of the inverter is usually affected by input current fluctuations, load changes, and the system operating environment. By monitoring the input and output power of the inverter, its performance during abnormal peak currents can be evaluated. If the inverter operates under abnormal current conditions, it may lead to a decrease in conversion efficiency or even overload. Therefore, monitor the efficiency of the inverter and improve its stability and efficiency by adjusting parameters.

[0060] Equipment temperature monitoring, goal: to evaluate the impact of equipment temperature changes caused by spike current on the long-term reliability and efficiency of the equipment. Measures: monitor the temperature changes of inverters, transformers and other related equipment. When a spike current occurs, the sudden change in current may cause the equipment temperature to rise. Long-term temperature rise may cause equipment damage or reduced efficiency, so real-time temperature monitoring and fault warning are required.

[0061] Power loss analysis, Objective: Analyze the system loss caused by abnormal peak current, especially the loss in power transmission. Measures: Evaluate the line loss and equipment loss caused by abnormal peak current. Calculate the power loss by monitoring the current waveform and voltage waveform, and take measures to reduce the loss.

[0062] System stability and safety assessment, Objective: To assess the impact of abnormal peak current on the stability and safety of the transmission line and the system as a whole. Measures: Analyze the impact of peak current on transmission lines, transformers, switchgear and other equipment, and assess whether it will cause equipment failure, line overload or shutdown. Through simulation analysis and real-time monitoring, predict possible failures and take preventive measures.

[0063] Inverter protection mechanism and response time, Objective: Evaluate the inverter's protection mechanism and response time for peak current. Measures: Analyze whether the inverter has protection functions for peak current, such as overload protection, overheat protection, short circuit protection, etc. Check the time it takes for the inverter to respond to peak current to ensure that the system can self-repair or switch to standby mode in the shortest time to prevent equipment damage.

[0064] Fault diagnosis and prediction, Goal: To diagnose faults through abnormal peak current data and predict potential equipment or system failures. Measures: Use data analysis and artificial intelligence algorithms to analyze abnormal peak current data and identify potential equipment failure modes. Based on historical data and current current fluctuations, predict possible equipment failures so that maintenance or replacement measures can be taken in advance.

[0065] Load forecasting and scheduling optimization, Objective: To evaluate the impact of abnormal peak current on load forecasting and scheduling optimization. Measures: Abnormal peak current will affect the accuracy of load forecasting. Through real-time data monitoring, the load forecasting model is corrected to ensure that the power grid load scheduling optimization can adapt to abnormal situations and avoid system instability caused by load forecasting errors.

[0066] Power quality monitoring, goal: to evaluate the impact of abnormal peak current on power quality, including harmonics, frequency fluctuations, etc. Measures: Abnormal peak current may cause harmonic pollution, frequency fluctuations and other power quality problems in the power grid. Through high-precision power quality monitoring instruments, real-time detection of power quality ensures that the harmonics and frequency of the power grid are within the standard range.

[0067] These measures work together to help fully understand the impact of abnormal peak currents on power system stability, equipment performance, and energy efficiency, and help take effective control and protection measures to improve the stability and reliability of the power system's distribution network energy storage.

[0068] Furthermore, according to the peak current anomaly assessment model, the obtained natural factor influence weights, the obtained human factor influence weights and the obtained battery internal resistance unblocking rate influence weights are analyzed, and then the peak current abnormal inverter working efficiency is output, thereby realizing the maintenance instructions for preventing the distribution network energy storage device from leaking.

[0069] In a second aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, and when the program instructions are executed, they are used to execute a stability control method for energy storage in a power system distribution network.

[0070] In a third aspect, the present invention provides a stability control system for energy storage in a power system distribution network, comprising a processor and a computer-readable storage medium.

[0071] like Figure 2 As shown, a stability control system for energy storage in a power distribution network of a power system, the system comprises:

[0072] A module for acquiring load fluctuation intervals of different power supply groups, wherein the module for acquiring load fluctuation intervals of different power supply groups of the energy storage integrated management platform for charging and discharging power per unit time is used to extract and analyze parameter features of load demand, grid frequency, grid faults and power factor of the power system, and to acquire load fluctuation intervals of different power supply groups of the energy storage integrated management platform for charging and discharging power per unit time;

[0073] A fault repair efficiency monitoring index generation module for an energy storage integrated management platform, the fault repair efficiency monitoring index generation module for an energy storage integrated management platform is used to generate a fault repair efficiency monitoring index for an energy storage integrated management platform through load fluctuation intervals of different power supply groups of charging and discharging power per unit time of the energy storage integrated management platform;

[0074] An active power and reactive power unit time jump range module, which is used to obtain the active power and reactive power unit time jump range of the power system by using the distribution network energy storage device leakage monitoring and stability control platform;

[0075] A stability control encrypted coding signal module, which is used to send the active power and reactive power unit time jump range and the energy storage integrated management platform fault repair efficiency monitoring index to the terminal of the distribution network visualization centralized control center, and use the distribution network visualization centralized control center to analyze and obtain the stability control encrypted coding signal;

[0076] A module for generating load fluctuation intervals under different stability control, wherein the module is used to generate load fluctuation intervals under different stability control using the stability control encrypted coding signal;

[0077] A node acquisition module for the cause of leakage of different distribution network energy storage devices, the node acquisition module for the cause of leakage of different distribution network energy storage devices is used to input the different stability control load fluctuation intervals into the stability control analysis algorithm for preventing leakage of distribution network energy storage devices of the chemical, mechanical, and thermal energy storage integrated management platform for different power supply groups, and obtain the nodes for the cause of leakage of different distribution network energy storage devices according to the stability control analysis algorithm for preventing leakage of distribution network energy storage devices of the chemical, mechanical, and thermal energy storage integrated management platform for different power supply groups, wherein the nodes for the cause of leakage of different distribution network energy storage devices correspond to the different stability control load fluctuation intervals;

[0078] A distribution network energy storage device leakage prevention maintenance instruction module is used to issue a distribution network energy storage device leakage prevention maintenance instruction to the power system through the different distribution network energy storage device leakage cause nodes.

[0079] Through the above detailed description of a stability control method, medium and system for energy storage in a distribution network of an electric power system, those skilled in the art can clearly know a stability control method, medium and system for energy storage in a distribution network of an electric power system in this embodiment. For the device disclosed in the embodiment, since it corresponds to the system disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the system part.

[0080] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for regulating the stability of energy storage in a power distribution network, characterized in that: The method includes: Step S100, by extracting and analyzing parameter features of load demand, grid frequency, grid fault and power factor of the power system, the load fluctuation range of different power supply groups of the energy storage integrated management platform for charging and discharging power per unit time is obtained; Step S200, generating a fault repair efficiency monitoring index for the energy storage integrated management platform through the load fluctuation range of different power supply groups of the charging and discharging power per unit time of the energy storage integrated management platform, and using the leakage monitoring and stability control platform of the distribution network energy storage device to obtain the jump range of the active power and reactive power per unit time of the power system; Step S300: sending the active power and reactive power jump range per unit time and the energy storage integrated management platform fault repair efficiency monitoring index to the terminal of the distribution network visualization centralized control center, using the distribution network visualization centralized control center to analyze and obtain a stability control encrypted coding signal, and using the stability control encrypted coding signal to generate different stability control load fluctuation ranges; Step S400, inputting the different stability control load fluctuation intervals into the stability control analysis algorithm for preventing leakage of distribution network energy storage devices of the chemical, mechanical and thermal energy storage integrated management platform for different power supply groups, and obtaining leakage cause nodes of different distribution network energy storage devices according to the stability control analysis algorithm for preventing leakage of distribution network energy storage devices of the chemical, mechanical and thermal energy storage integrated management platform for different power supply groups, wherein the leakage cause nodes of different distribution network energy storage devices correspond to the different stability control load fluctuation intervals; Step S500: issuing a maintenance instruction to prevent the distribution network energy storage device from leaking to the power system through the different distribution network energy storage device leakage cause nodes.

2. A method for regulating the stability of energy storage in a power distribution network of a power system according to claim 1, characterized in that: The system further comprises: Input the different stability control load fluctuation intervals into the stability control analysis algorithm for preventing leakage of distribution network energy storage devices of the different power supply groups' chemical, mechanical, and thermal energy storage integrated management platform, wherein the stability control analysis algorithm for preventing leakage of distribution network energy storage devices of the different power supply groups' chemical, mechanical, and thermal energy storage integrated management platform includes different algorithms, and the different algorithms include a current frequency unit time prediction model, a battery internal resistance withstand response model, and a distribution network energy storage device leakage level prediction model; According to the stability control analysis algorithm of the chemical, mechanical and thermal energy storage integrated management platform for preventing leakage of distribution network energy storage devices of different power supply groups, the power system is analyzed for the leakage cause nodes of the distribution network energy storage devices of the peak current anomaly-energy storage integrated management platform fault repair efficiency, and the leakage cause nodes of the different distribution network energy storage devices are output. Each distribution network energy storage device leakage cause node corresponds to each stability control load fluctuation interval. The maintenance instructions for preventing leakage of distribution network energy storage devices generated by each distribution network energy storage device leakage cause node are output.

3. A method for regulating the stability of energy storage in a power distribution network of a power system as claimed in claim 2, characterized in that: The system further comprises: Input the load fluctuation intervals of different stability control into the current frequency unit time prediction model, perform stability control of different power supply groups, and obtain unit time data matrices of different power supply groups, including data of different power supply groups before and after the peak current anomaly of the energy storage integrated management platform; According to the battery internal resistance tolerance response model, the data of different power supply groups before the peak current abnormality of the energy storage integrated management platform and the data of different power supply groups after the peak current abnormality of the energy storage integrated management platform are compared to evaluate the battery internal resistance load operation data before and after the peak current abnormality; According to the distribution network energy storage device leakage level prediction model, the peak current anomaly impact level analysis is performed on the battery internal resistance load operation data before and after the peak current anomaly, and the leakage cause nodes of the different distribution network energy storage devices are output.

4. A method for regulating the stability of energy storage in a power distribution network of a power system according to claim 1, characterized in that: The system further comprises: According to the leakage cause nodes of the different distribution network energy storage devices, a collaborative analysis of the energy storage and the smart grid is performed on the energy storage integrated management platform to obtain a collaborative coefficient of the energy storage and the smart grid; it is determined whether the collaborative coefficient of the energy storage and the smart grid exceeds the preset collaborative coefficient of the energy storage and the smart grid, and when the collaborative coefficient of the energy storage and the smart grid exceeds the preset collaborative coefficient of the energy storage and the smart grid, a leakage impact assessment coefficient is obtained; and feedback calculation is performed on the leakage cause nodes of the different distribution network energy storage devices using the leakage impact assessment coefficient to obtain impact weights of the leakage cause nodes of the different distribution network energy storage devices.

5. A method for regulating the stability of energy storage in a power distribution network of a power system as claimed in claim 4, characterized in that: Determining whether the synergy coefficient between the energy storage and the smart grid exceeds a preset synergy coefficient between the energy storage and the smart grid, the system further includes: When the synergy coefficient of the energy storage and the smart grid is lower than the preset synergy coefficient of the energy storage and the smart grid, a key supervision signal is obtained; according to the key supervision signal, the stability control load fluctuation interval of the synergy coefficient of the energy storage and the smart grid is labeled, the labeled stability control load fluctuation interval is obtained, and the labeled stability control load fluctuation interval is focused on per unit time.

6. A method for regulating the stability of energy storage in a power distribution network of a power system according to claim 1, characterized in that: The maintenance instruction for preventing the energy storage device of the distribution network from leaking is issued to the power system through the different energy storage device leakage cause nodes of the distribution network, including: Collect discharge depth information of the power system; distribute the discharge depths at different locations in the discharge depth information to corresponding peak current abnormal distribution positions to generate peak current abnormal transmission line data; perform peak current abnormality assessment on preset line nodes in the peak current abnormal transmission line data to obtain peak current abnormal inverter working efficiency; issue a maintenance instruction to prevent distribution network energy storage device leakage to the power system based on the peak current abnormal inverter working efficiency and the leakage cause nodes of the different distribution network energy storage devices.

7. A method for regulating the stability of energy storage in a power distribution network of a power system as claimed in claim 6, characterized in that: Performing peak current anomaly evaluation on preset line nodes in the peak current abnormality transmission line data to obtain the peak current abnormality inverter working efficiency, further comprising: Line voltage fluctuations: evaluate voltage fluctuations caused by abnormal peak currents and their impact on system stability, monitor voltage fluctuations, and check whether the voltage exceeds the normal range when a peak current occurs at a preset line node. If the voltage fluctuation is too large, adjust it through an inverter or other power equipment; Current and power factor analysis: analyze the power factor fluctuation caused by peak current, evaluate the operating efficiency of the power system, calculate and analyze the power factor change, whether the power factor is lower than the standard value during the peak current, and adjust the reactive power output of the inverter to optimize the power factor and improve the system efficiency; Inverter efficiency evaluation: evaluate the working efficiency of the inverter under abnormal peak current conditions. The efficiency of the inverter is usually affected by input current fluctuations, load changes and system operating environment. By monitoring the input and output power of the inverter, its working performance during abnormal peak current is evaluated. If the inverter works under abnormal current conditions, the conversion efficiency will decrease and overload will occur. Monitor the efficiency of the inverter and improve its stability and efficiency by adjusting parameters. Equipment temperature monitoring: evaluate the impact of equipment temperature changes caused by peak current on the long-term reliability and efficiency of the equipment, monitor the temperature changes of inverters, transformers and other related equipment. When a peak current occurs, the sudden change in current causes the equipment temperature to rise. Long-term temperature rise can cause equipment damage or reduced efficiency. Real-time temperature monitoring and fault warning are carried out; Power loss analysis: analyze the system loss caused by abnormal peak current and the loss in power transmission, evaluate the line loss and equipment loss caused by abnormal peak current, calculate the power loss by monitoring the current waveform and voltage waveform, and take measures to reduce the loss; System stability and safety assessment: assess the impact of abnormal peak current on the overall stability and safety of the transmission line and system, analyze the impact of peak current on the transmission line, transformer, and switchgear, assess whether it will cause equipment failure, line overload, or shutdown, predict failures and take preventive measures through simulation analysis and real-time monitoring; Inverter protection mechanism and response time: evaluate the inverter's protection mechanism and response time against peak current, analyze whether the inverter has the protection function against peak current, check the time it takes the inverter to respond to peak current, and ensure that the system can self-repair or switch to standby mode in the shortest time to prevent equipment damage; Fault diagnosis and prediction: Fault diagnosis is performed through abnormal peak current data to predict potential equipment failures or system failures. Data analysis and artificial intelligence algorithms are used to analyze abnormal peak current data to identify potential equipment failure modes. Based on historical data and current current fluctuations, equipment failures can be predicted and maintenance or replacement measures can be taken in advance. Load forecasting and scheduling optimization: evaluate the impact of abnormal peak current on load forecasting and scheduling optimization. Abnormal peak current will affect the accuracy of load forecasting. Through real-time data monitoring, the load forecasting model is corrected to ensure that the power grid load scheduling optimization can adapt to abnormal situations and avoid system instability caused by load forecasting errors. Power quality monitoring evaluates the impact of abnormal peak current on power quality, including harmonics and frequency fluctuations. Abnormal peak current causes harmonic pollution and frequency fluctuation power quality problems in the power grid. Through high-precision power quality monitoring instruments, power quality is detected in real time to ensure that the harmonics and frequency of the power grid are within the standard range.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program instructions, which, when executed, are used to execute the method for stability control of energy storage in a distribution network of a power system according to any one of claims 1 to 8.

9. A stability control system for energy storage in a power distribution network, characterized in that: A computer program product having a processor and comprising the computer-readable storage medium according to claim 8.

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