Power grid management system based on distributed energy storage

Abstract

The present invention relates to the field of intelligent power systems, and in particular to a power grid management system based on distributed energy storage, including collecting status information of distributed energy storage devices in real time through intelligent meters, sensors, and measuring equipment, acquiring and monitoring key parameters in the power grid, and performing preprocessing operations on the collected data; constructing a power grid status analysis model through a deep learning algorithm based on the preprocessed power grid data information, and obtaining an output result of the power grid status analysis model; establishing an optimal distributed energy storage strategy in combination with the output result of the power grid status analysis model, managing the power grid, making energy utilization efficient and stable, and increasing the flexibility and responsiveness of the power grid management system.

Description

Technical Field

[0001] The present invention relates to the field of intelligent power systems, and in particular to a power grid management system based on distributed energy storage. Background Art

[0002] Distributed energy storage technology refers to the distribution of energy storage equipment at various nodes of the power system, and the optimization of the power system operation is achieved through centralized management and control. The traditional power grid system adopts a centralized power generation and transmission mode, which has problems such as energy waste, large transmission losses, and unstable power quality. For example, during peak hours, it is necessary to adjust the output of the generator set to meet demand, which will lead to energy waste; when encountering a fault or network power outage, it is easy to cause a large-scale power outage.

[0003] The grid management system based on distributed energy storage can effectively solve these problems and achieve efficient, stable and sustainable development of the power system. Summary of the invention

[0004] The distributed energy storage-based power grid management system provided by the present invention aims to utilize the distributed energy storage system to dispersely store and release energy in the power grid to balance power supply and demand, adjust frequency and maintain voltage stability; through intelligent control and optimization algorithms, the power grid is managed to improve the reliability, stability and sustainability of the power grid, and the effective management and coordinated operation of distributed energy storage resources are realized to achieve efficient power grid operation.

[0005] The technical solution of the present invention is specifically as follows:

[0006] The power grid management system based on distributed energy storage includes the following:

[0007] Data collection module, data preprocessing module, power grid status analysis module, intelligent energy management module, distributed energy storage strategy module, power grid fault monitoring module;

[0008] The data collection module is used to collect grid data through sensors and monitoring equipment; collect status information of distributed energy storage devices; and transmit the obtained data information to the system for further processing and analysis;

[0009] The data preprocessing module processes and cleans the raw data obtained from the data collection module to improve data quality and provide data support for subsequent analysis and decision-making;

[0010] The power grid status analysis module is used to analyze and evaluate the status of the power grid. By monitoring and analyzing the power grid status, the power grid status can be understood in a timely manner to ensure the stable operation of the power grid.

[0011] The intelligent energy management module acquires and analyzes characteristic parameters based on historical data preset energy demand, obtained energy supply, and real-time electricity market price changes, formulates the best distributed energy storage strategy, and deeply understands the operation of the power grid;

[0012] Distributed energy storage strategy module, which is used to formulate distributed energy storage strategies according to the state of the power grid, so as to help the power grid system manage energy storage resources by minimizing the total cost of the distributed energy storage system and the stability of efficient energy utilization results;

[0013] The power grid fault monitoring module can detect possible faults in the power grid in a timely manner through real-time monitoring and analysis, and take corresponding measures; at the same time, when a distributed energy storage device fails or fails, the system can accelerate detection and automatically transfer energy storage and supply to ensure the safe operation of the power grid.

[0014] The power grid management method based on distributed energy storage includes the following steps:

[0015] Step S1. Through smart meters, sensors, and measuring equipment, the status information of distributed energy storage devices is collected in real time, and key parameters in the power grid are acquired and monitored, and the collected data is preprocessed;

[0016] Step S2. Based on the preprocessed power grid data information, a power grid state analysis model is constructed through a deep learning algorithm to obtain an output result of the power grid state analysis model;

[0017] Step S3. Combined with the output results of the grid state analysis model, an optimal distributed energy storage strategy is established to manage the grid so that energy utilization is efficient and stable, and the flexibility and responsiveness of the grid management system are increased.

[0018] Further, step S1 specifically includes:

[0019] When preprocessing the collected data, standardization and smoothing are performed to extract the trend and periodicity information of the power grid data, and then normalization is performed.

[0020] Furthermore, the power grid state analysis model MOD is specifically expressed as:

[0021] MOD=Sta,Yt,Qa,Wb,Anl

[0022] Among them, Data represents the set of environmental weather parameters that affect the state of the power grid; Yt represents the power generation capacity of the power grid; Wb represents the set of power supply load conditions; Qa represents the voltage level of the power transmission line in the power grid; Anl represents the output result of the power grid state analysis model.

[0023] Further, in step S3, when constructing the optimal distributed energy storage strategy, the required energy is preset as E0 according to historical data, and the energy supply obtained is E g , and the real-time electricity market price change is Δpri, the required characteristic parameters are obtained, and the specific process is as follows:

[0024]

[0025] Among them, Q(t) represents the average operating cost from time t to t+1; Re(t0) represents the accumulated cost of the distributed energy storage system at time t0; Eg(t0) represents the energy supply obtained at time t0; E0t0 represents the energy required at time t0; δ1 and δ2 represent the adjustment coefficients.

[0026] Furthermore, an objective function of the optimal distributed energy storage strategy is constructed, and then constraints on charging and discharging of the energy storage system are set; the solution is obtained by integrating linear programming, integer programming or dynamic programming methods.

[0027] Furthermore, the constraints are expressed as:

[0028]

[0029] Where, E(t represents the amount of electrical energy stored at time t; η char Indicates charging efficiency; η dis_char represents the discharge efficiency; Co represents the maximum energy storage capacity of the energy storage system; P max_char Indicates the maximum limit of charging power; P max_dis_char Indicates the maximum limit of discharge power; P char Represents the charging power of the distributed energy storage system; P dis_char Represents the discharge power of the distributed energy storage system.

[0030] Beneficial effects:

[0031] 1. The present invention improves data quality, reduces potential errors and deviations, makes data more reliable and accurate, and more in line with actual conditions by preprocessing power grid-related data information, reduces inaccurate and misleading information, and helps to obtain further accurate analysis results; smoothing technology can effectively reduce noise and fluctuations in power grid data caused by measurement errors, sensor interference or other random factors, help extract periodic information in power grid data, make periodic changes more obvious, and help reveal potential periodic patterns and laws in the data; by normalizing data to the same dimension and scale, imbalance can be eliminated, so that each parameter has a relatively balanced weight in the analysis and modeling process, and the data range is scaled to a similar interval, which accelerates the convergence speed of the algorithm and improves the calculation efficiency, while reducing the impact of numerical differences and improving the stability and reliability of the model.

[0032] 2. The present invention can learn the characteristics and patterns of a large amount of data by constructing a power grid state analysis model, extract potential information from complex power grid data, and improve the model's expressiveness and generalization capabilities; by inputting real-time data, the deep learning model can quickly calculate and infer, output the analysis results or monitoring indicators of the power grid state, help monitor the power grid state in real time, predict potential problems, and take corresponding measures to adjust and optimize in time. Power grid data may drift or change with time and environmental changes. Traditional models may not be able to adapt to such changes, while deep learning models can adjust the model's weights and parameters by learning new data to maintain the model's adaptability and accuracy.

[0033] 3. The invention establishes the best distributed energy storage strategy based on the output results of the grid state analysis model. By minimizing the total cost of the distributed energy storage system, the investment and operating costs of the energy storage equipment can be reduced, which helps to improve the economic benefits of the grid management system and reduce the burden. In addition, stability and efficient energy utilization are the goals of the best distributed energy storage strategy. By comprehensively considering the output results of the grid state analysis model and load changes, the energy storage and release strategies of the energy storage system can be optimized to make energy utilization more efficient and stable, minimize energy waste, improve energy utilization efficiency, and reduce dependence on traditional energy. The best distributed energy storage strategy can also enhance the flexibility and responsiveness of the grid management system. According to changes in the grid state and adjustments in demand, the energy storage and release strategies can be flexibly adjusted to adapt to changes in the grid and respond to emergencies. At the same time, it can promote the development and utilization of sustainable energy, promote the popularization of clean energy and reduce dependence on traditional energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a flow chart of the power grid management method based on distributed energy storage of the present invention;

[0035] Figure 2 It is a module diagram of the power grid management system based on distributed energy storage of the present invention. DETAILED DESCRIPTION

[0036] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. It should also be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0037] Multiple energy storage devices, such as lithium-ion batteries, supercapacitors, etc., are distributed in the power grid to store and release electrical energy. These energy storage devices can be distributed in different geographical locations to better cope with energy fluctuations and demand changes in the power grid.

[0038] In this embodiment, there is a difference between energy supply and demand in the power grid, and the installation of distributed energy storage devices can store and release electric energy in multiple geographical locations to achieve energy balance and optimization. First, select appropriate energy storage devices, such as lithium-ion batteries and supercapacitors, which have the characteristics of high energy density and fast response, and can achieve efficient energy storage and release; second, determine the best device deployment strategy, and distribute the energy storage devices in different geographical locations according to the topological structure and load distribution of the power grid to achieve energy balance and optimization, for example, install energy storage devices at key nodes, power substations, and user concentrated areas of the power grid; then, establish a communication and control system for distributed energy storage devices, and each energy storage device should be connected to a central control system to achieve real-time monitoring and control of its state and energy storage. Specifically, the control system can use modern communication technologies such as the Internet of Things (IoT) and cloud computing to achieve remote monitoring and management; optimize the charging and discharging strategy of the energy storage device, and according to the energy fluctuations of the power grid and the changes in user needs, the intelligent control system can adjust the charging and discharging rate and time of the energy storage device according to real-time data and algorithm models to achieve energy balance and optimal utilization.

[0039] See attached Figure 1 , this embodiment provides a power grid management method based on distributed energy storage, comprising the following steps:

[0040] S1. Through smart meters, sensors, measuring equipment and communication technology, the status information of distributed energy storage devices, such as battery capacity, charge and discharge rate, temperature, etc., is collected in real time, and key parameters in the power grid are acquired and monitored, including power grid load, battery status, energy storage status, voltage, etc. The obtained power grid-related data is transmitted to the central control system after data preprocessing, laying the foundation for subsequent analysis and decision-making.

[0041] Preprocess the grid-related data information and clean the collected data using existing technologies, including removing missing values, outliers and noise. Standardize the grid data of different units and scales to the same dimension. The process is as follows:

[0042]

[0043] Among them, Di u represents the standardized power grid data information; Y represents the recorded original power grid data information; μ u represents the time series mean of power grid data information; b u represents the standard deviation of the power grid data information time series; u represents the time series.

[0044] Then, smoothing is applied to the power grid data with time series to reduce noise and fluctuations and extract trend and periodicity information. The specific process is as follows:

[0045]

[0046] Where R represents the smoothed grid data at the TIM time step; Di u (ti) represents the standardized power grid data information at time ti; ω m represents the weight parameter, which is used to further increase the accuracy of the smoothing process; M represents the total amount of data information after standardization; s1 represents the smoothing factor, which is a parameter used to control the degree of smoothing.

[0047] By normalizing the power grid data, we can ensure that the weights of different parameters are relatively balanced and avoid some parameters from having too great an impact on the analysis results. The specific process is as follows:

[0048]

[0049] Among them, X represents the evaluation of the overall stability of the power grid data; μ represents the constant value adjustment parameter to ensure the stability of the normalization process; Represents positive numbers to prevent extreme denominators from hindering the calculation process.

[0050] The present invention improves data quality, reduces potential errors and deviations, makes data more reliable and accurate, and more in line with actual conditions by preprocessing power grid-related data information, reduces inaccurate and misleading information, and helps to obtain further accurate analysis results; smoothing technology can effectively reduce noise and fluctuations in power grid data caused by measurement errors, sensor interference or other random factors, help extract periodic information in power grid data, make periodic changes more obvious, and help reveal potential periodic patterns and laws in the data; by normalizing data to the same dimension and scale, imbalance can be eliminated, so that each parameter has a relatively balanced weight in the analysis and modeling process, and the data range is scaled to a similar interval, which accelerates the convergence speed of the algorithm and improves calculation efficiency, while reducing the impact of numerical differences and improving the stability and reliability of the model.

[0051] S2. Based on the data from distributed energy storage devices, the central control system uses advanced algorithms and models to monitor, analyze and optimize the power grid.

[0052] According to the preprocessed power grid data information, the power grid state analysis model MOD is constructed through the deep learning algorithm. The specific process is as follows:

[0053] MOD=Sta,Yt,Qa,Wb,Anl

[0054] Among them, Data represents the set of environmental weather parameters that affect the state of the power grid; Yt represents the power generation capacity of the power grid; Wb represents the set of power supply load conditions; Qa represents the voltage level of the power transmission line in the power grid; Anl represents the output result of the power grid state analysis model. Specifically, the set of environmental weather parameters that affect the state of the power grid includes temperature, humidity, wind speed, and rainfall, that is, Sta = Sta1, Sta2, Sta3, Sta4, Sta1 represents the temperature parameter set, Sta2 represents the humidity parameter set, Sta3 represents the wind speed parameter set, Sta4 represents the rainfall parameter set, S1, S2, S3, S4 respectively represent the total number of elements in the corresponding subsets; the power supply load condition set includes load peak and total power supply load, that is, Wb = Wb1, Wb2, Wb1 represents the load peak set, and Wb2 represents the overall load condition of the power grid.

[0055]

[0056] Among them, C s represents the monitoring coefficient of the sth rainfall recording device; T represents the total time series; Th represents the impact coefficient of thunderstorm activity; Sta1t represents the temperature at the current moment; Indicates the humidity recorded by the sth device; Sta1' indicates the expected temperature; represents the recorded wind speed of the sth device; G(Yt) represents the stable function of the power grid generation capacity to maintain the power grid state; represents the mean of the peak load set of the power grid; ω1 and ω2 represent weight coefficients, which are used to determine the relative importance of the overall load of the power grid and the peak load set; F(Qa) represents the influence function of the voltage level of the power grid transmission line on the power grid state; ρ represents the adjustment parameter; max_Wb1 represents the maximum value in the peak load set; res_Wb2 represents the safety upper limit set during the operation of the power grid.

[0057] The present invention can learn the characteristics and patterns of a large amount of data by constructing a power grid state analysis model, extract potential information from complex power grid data, and improve the model's expressiveness and generalization capabilities; by inputting real-time data, the deep learning model can quickly calculate and infer, output the analysis results or monitoring indicators of the power grid state, help monitor the power grid state in real time, predict potential problems, and take corresponding measures to adjust and optimize in time. Power grid data may drift or change with time and environmental changes. Traditional models may not be able to adapt to such changes, while deep learning models can adjust the model's weights and parameters by learning new data to maintain the model's adaptability and accuracy.

[0058] S3. The central control system will balance the energy supply and demand of the power grid according to the output results of the power grid state analysis model and load changes, establish the best distributed energy storage strategy, quickly respond to power grid frequency changes, and provide frequency regulation and stabilization services to ensure the stable operation of the power grid and the quality of power. In addition, the system will also consider the volatility of renewable energy and optimize the charging and discharging behavior of energy storage devices to maximize the use of renewable energy.

[0059] First, according to historical data, the required energy is preset as E0, and the energy supply obtained is E g , and the real-time electricity market price change is Δpri, the characteristic parameters required for the optimal distributed energy storage strategy are obtained. The specific process is as follows:

[0060]

[0061] Where Q(t) represents the average operating cost from time t to time t+1; Re(t0) represents the accumulated cost of the distributed energy storage system at time t0; E g (t0) represents the energy supply obtained at time t0; E0(t0) represents the energy required at time t0; δ1 and δ2 represent the adjustment coefficients.

[0062] Then, the objective function of the optimal distributed energy storage strategy is constructed. The specific process is as follows:

[0063]

[0064] Among them, U(Q) represents the objective function, which helps the power grid management system to dispatch energy storage resources more effectively by minimizing the total cost of the distributed energy storage system and the stability between the efficient energy utilization results; P char Represents the charging power of the distributed energy storage system; P dis_char Represents the discharge power of the distributed energy storage system; Cost char Represents the charging cost of the distributed energy storage system; Pro dis_char represents the discharge benefit of the distributed energy storage system; τ' represents the strategy optimization factor; τ″ represents the sensitivity factor, which evaluates the sensitivity of different parameters to the objective function; Anl represents the output result of the grid state analysis model; θ1 represents the influence coefficient; O(t) represents the influence function of the grid state on energy utilization at time t; η pow represents the efficiency of electric energy utilization; θ2 represents the efficiency coefficient, which is used to evaluate the performance and electric energy utilization of the power grid management system; Wi(t) represents the stability requirement of the distributed energy storage system.

[0065] The constraints for charging and discharging of the energy storage system are expressed as:

[0066]

[0067] Where E(t) represents the amount of electrical energy stored at time t; η char Indicates charging efficiency; η dis_char represents the discharge efficiency; Co represents the maximum energy storage capacity of the energy storage system; P max_char Indicates the maximum limit of charging power; P max_dis_char Indicates the maximum limit of discharge power.

[0068] Energy storage system characteristics include energy storage capacity, charging and discharging efficiency, and energy storage rate. These parameters affect the energy scheduling and performance of energy storage systems. Energy storage capacity is the total amount of electrical energy that can be stored by energy storage devices in the power system, such as battery packs and energy storage power stations. Energy storage systems can store excess electrical energy and release it when needed to balance the difference between grid load and supply and demand, and provide backup energy and peak and frequency regulation functions.

[0069] In the above optimization problem, the optimal distributed energy storage system operation strategy is obtained by integrating linear programming, integer programming or dynamic programming methods.

[0070] At the same time, the grid management system also has fault monitoring and automatic recovery mechanisms. When a distributed energy storage device fails or fails, the system can quickly detect and automatically transfer energy storage and supply to ensure the continuous operation and reliability of the grid.

[0071] The present invention establishes the best distributed energy storage strategy based on the output results of the grid state analysis model. By minimizing the total cost of the distributed energy storage system, the investment and operating costs of the energy storage equipment can be reduced, which helps to improve the economic benefits of the grid management system and reduce the burden. In addition, stability and efficient energy utilization are the goals of the best distributed energy storage strategy. By comprehensively considering the output results of the grid state analysis model and load changes, the energy storage and release strategies of the energy storage system can be optimized, making energy utilization more efficient and stable, minimizing energy waste, improving energy utilization efficiency, and reducing dependence on traditional energy. The best distributed energy storage strategy can also enhance the flexibility and responsiveness of the grid management system. According to the changes in the grid state and the adjustment of demand, the energy storage and release strategies can be flexibly adjusted to adapt to changes in the grid and respond to emergencies. At the same time, it can promote the development and utilization of sustainable energy, promote the popularization of clean energy and reduce dependence on traditional energy.

[0072] See attached Figure 2 , this embodiment provides a power grid management system based on distributed energy storage, including the following contents:

[0073] Data collection module, data preprocessing module, power grid status analysis module, feature acquisition module, distributed energy storage strategy module, power grid fault monitoring module;

[0074] The data collection module is used to collect grid-related data, including grid load, battery status, energy storage status, voltage, etc., through sensors, smart meters, monitoring equipment, etc.; and collect status information of distributed energy storage devices, such as battery capacity, charge and discharge rate, temperature, etc.; and transmit the obtained data information to the system for further processing and analysis.

[0075] The data preprocessing module processes and cleans the raw data obtained from the data collection module, including removing outliers, filling missing data, data smoothing and other operations, to ensure the accuracy and reliability of subsequent analysis, improve data quality, and provide data support for subsequent analysis and decision-making.

[0076] The power grid status analysis module is used to analyze and evaluate the status of the power grid. Through monitoring and analysis of the power grid status, the power grid status can be timely understood, corresponding treatment measures can be taken, and a data basis can be provided for the follow-up to ensure the stable operation of the power grid.

[0077] The intelligent energy management module acquires and analyzes characteristic parameters based on historical data preset energy demand, obtained energy supply, and real-time electricity market price changes, formulates the best distributed energy storage strategy, deeply understands the operation of the power grid, and provides reference for subsequent distributed energy storage strategies.

[0078] The distributed energy storage strategy module is used to formulate a distributed energy storage strategy based on the state of the power grid, so as to help the power grid system manage energy storage resources by minimizing the total cost of the distributed energy storage system and the stability between efficient energy utilization results.

[0079] The power grid fault monitoring module can detect possible faults in the power grid in a timely manner through real-time monitoring and analysis, and take corresponding measures; at the same time, when a distributed energy storage device fails or fails, the system can quickly detect and automatically transfer energy storage and supply to ensure the safe operation of the power grid.

[0080] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0081] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0082] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0083] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0084] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A power grid management system based on distributed energy storage, characterized in that: Includes the following: Data collection module, data preprocessing module, power grid status analysis module, intelligent energy management module, distributed energy storage strategy module, power grid fault monitoring module; The data collection module is used to collect power grid data through sensors and monitoring equipment; and collect status information of distributed energy storage devices; and transmit the obtained data information to the system for further processing and analysis; The data preprocessing module processes and cleans the raw data obtained from the data collection module to improve data quality and provide data support for subsequent analysis and decision-making; The power grid status analysis module is used to analyze and evaluate the status of the power grid, and timely understand the status of the power grid through monitoring and analysis of the power grid status to ensure the stable operation of the power grid; Calculate the output of the grid state analysis model : ； in, Indicates Monitoring factor of rainfall recording equipment; represents the total time series; Represents the impact coefficient brought by thunderstorm activities; Indicates the current temperature situation; Indicates Humidity conditions recorded by each device; Indicates expected temperature conditions; Indicates The wind speed recorded by each device; represents the rainfall parameter set; A stable function that represents the power generation capacity of the power grid to maintain the state of the power grid; Indicates the overall load of the power grid; It represents the mean value of the peak value of the power grid load; , It represents the weight coefficient, which is used to determine the relative importance of the overall load of the power grid and the set of load peaks; A function representing the influence of the voltage level of the power grid transmission line on the power grid state; Indicates adjustment parameters; Indicates the maximum value in the load peak set; represents the safety upper limit set during the operation of the power grid; , Respectively represent the total number of elements in the corresponding temperature set and rainfall set; The intelligent energy management module presets energy requirements based on historical data , the energy supply obtained and real-time electricity market price changes factors, acquire and analyze characteristic parameters, formulate the best distributed energy storage strategy, and deeply understand the operation of the power grid; Calculate the required characteristic parameters. The specific process is as follows: ； in, Indicated in Time has come Average operating costs within Indicates that the distributed energy storage system The amount of accumulated cost at the time Indicated in The energy supply available at all times; Indicated in The energy you need at all times; , represents the adjustment coefficient; The distributed energy storage strategy module is used to formulate a distributed energy storage strategy based on the state of the power grid, so as to minimize the total cost of the distributed energy storage system and the stability between the efficient energy utilization results, and help the power grid system manage energy storage resources; The objective function of constructing the optimal distributed energy storage strategy is as follows: ； in, Represent the objective function, which is the stability between minimizing the total cost of the distributed energy storage system and the efficient energy utilization results; Indicates the charging power of the distributed energy storage system; Represents the discharge power of the distributed energy storage system; represents the charging cost of the distributed energy storage system; represents the discharge benefit of the distributed energy storage system; represents the strategy optimization factor; Represents the sensitivity factor, which evaluates the sensitivity of different parameters to the objective function; represents the influence coefficient; Indicated in The influence function of the power grid state on energy utilization; Indicates the efficiency of electric energy utilization; It represents the efficiency coefficient, which is used to evaluate the performance and energy utilization of the power grid management system; Represents the stability requirements of the distributed energy storage system; The power grid fault monitoring module can detect possible faults in the power grid in a timely manner through real-time monitoring and analysis, and take corresponding measures; at the same time, when a distributed energy storage device fails or fails, the system can accelerate detection and automatically transfer energy storage and supply to ensure the safe operation of the power grid.

2. A power grid management method based on distributed energy storage, applied to the power grid management system based on distributed energy storage as claimed in claim 1, characterized in that: The following steps are involved: Step S1. Through smart meters, sensors, and measuring equipment, the status information of distributed energy storage devices is collected in real time, and key parameters in the power grid are acquired and monitored, and the collected data is preprocessed; Step S2. Based on the preprocessed power grid data information, a power grid state analysis model is constructed through a deep learning algorithm to obtain an output result of the power grid state analysis model; Step S3. Combined with the output results of the grid state analysis model, an optimal distributed energy storage strategy is established to manage the grid so that energy utilization is efficient and stable, and the flexibility and responsiveness of the grid management system are increased.

3. The power grid management method based on distributed energy storage according to claim 2 is characterized in that: The step S1 specifically includes: When preprocessing the collected data, standardization and smoothing are performed to extract the trend and periodicity information of the power grid data, and then normalization is performed.

4. The distributed energy storage-based power grid management method according to claim 2, characterized in that: The power grid status analysis model , specifically expressed as: ； in, represents a set of environmental weather parameters that affect the state of the power grid; Indicates the power generation capacity of the grid; Indicates the power supply load condition set; Indicates the voltage level of the power transmission line in the power grid; Represents the output results of the power grid status analysis model.

5. The distributed energy storage-based power grid management method according to claim 2, characterized in that: The step S3 specifically includes: The objective function of the optimal distributed energy storage strategy is constructed, and then the constraints of charging and discharging of the energy storage system are set; and the solution is obtained by integrating linear programming, integer programming or dynamic programming methods.

6. The distributed energy storage-based power grid management method according to claim 5, characterized in that: The constraints are expressed as: ； in, Indicates at time The amount of electrical energy stored at Indicates charging efficiency; Indicates discharge efficiency; Indicates the maximum energy storage capacity of the energy storage system; Indicates the maximum limit of charging power; Indicates the maximum limit of discharge power; Indicates the charging power of the distributed energy storage system; Represents the discharge power of the distributed energy storage system.

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