Energy storage networking control method, apparatus and device, and storage medium

Abstract

The invention relates to an energy storage networking control method and device, equipment and a storage medium, and the method comprises the following steps: carrying out the real-time data collection of a target power grid through an intelligent sensor, and obtaining the collection data of the power grid; predicting the power grid acquisition data through a preset big data analysis technology to obtain a power grid operation trend; performing energy state evaluation on the target energy storage system to obtain the available capacity of the target energy storage system; formulating a scheduling control plan based on the available capacity and the power grid operation trend; wherein the scheduling control plan is used for scheduling and controlling the target power grid or the target energy storage system so as to keep the frequency of the target power grid stable, and the technical problems of how to effectively integrate the energy storage system into an existing power network and how to ensure that the energy storage system can stably support the frequency of the power grid and other key performance indexes are solved.

Description

Technical Field

[0001] The present invention relates to the field of power grid technology, and in particular to an energy storage network control method device, equipment, and storage medium. Background Art

[0002] With the growing global demand for clean energy and the gradual depletion of traditional fossil fuel resources, the power system is undergoing a transformation from centralized power generation to distributed energy resources (DERs). In this process, the importance of energy storage systems as key components for balancing power supply and demand and improving grid flexibility has become increasingly prominent. However, the current operation mode of the power system is mainly based on relatively stable traditional power sources, and has limited adaptability to rapidly changing renewable energy inputs and fluctuating load demands. Therefore, how to effectively integrate energy storage systems into the existing power network and ensure that they can stably support grid frequency and other key performance indicators has become a technical problem that needs to be solved urgently.

[0003] A core issue in the research background is real-time performance and prediction accuracy. In order to achieve efficient energy storage scheduling, it is necessary to rely on accurate data acquisition and analysis technology to capture changes in the state of the power grid. Traditional data processing methods are difficult to meet the requirements of modern smart grids for real-time decision-making. Although emerging big data analysis and artificial intelligence algorithms provide new solutions, their application still faces challenges such as high computational complexity and insufficient model training data. In addition, the energy state assessment of the energy storage system is also a difficult point. It not only affects the safety and life of the energy storage equipment itself, but is also directly related to whether a reasonable scheduling plan can be formulated. Existing technologies still have a lot of room for improvement in accurately assessing the available capacity of energy storage systems, especially when faced with a variety of different types of energy storage devices.

[0004] Another important consideration is the effectiveness and adaptability of the energy storage grid control strategy. As more and more energy storage systems are connected to the grid, how to design a control method that can both ensure grid stability and maximize the benefits of the energy storage system has become a research focus. Current control strategies are mostly based on fixed rules or simple feedback mechanisms, which are incapable of coping with complex grid operation trends. For example, in the case of a sudden increase or decrease in renewable energy generation, these strategies may not be able to respond in time, resulting in grid frequency deviations exceeding the allowable range. To this end, it is necessary to develop a dynamic and highly adaptive energy storage grid control method to cope with the more flexible and complex power market environment and grid operation conditions in the future. This control method should not only consider short-term power balance, but also take into account long-term economy and environmental friendliness, and provide technical support for building a sustainable smart grid. Summary of the invention

[0005] The main purpose of the present invention is to provide a method, device, equipment, and storage medium for energy storage network control, which solves the technical problem of how to effectively integrate the energy storage system into the existing power network and ensure that it can stably support the grid frequency and other key performance indicators.

[0006] To achieve the above object, the present invention provides a method for controlling energy storage network construction, comprising the following steps: Real-time data collection of the target power grid is performed through intelligent sensors to obtain power grid collection data; The power grid collected data is predicted by using a preset big data analysis technology to obtain a power grid operation trend; Evaluate the energy status of the target energy storage system to obtain the available capacity of the target energy storage system; A dispatching control plan is formulated based on the available capacity and the operating trend of the power grid; wherein the dispatching control plan is used to dispatch and control the target power grid or target energy storage system to keep the frequency of the target power grid stable.

[0007] Furthermore, the smart sensor is a distributed optical fiber sensor array, and the real-time data collection of the target power grid is performed by the smart sensor to obtain the power grid collection data, including: Sampling key nodes of the target power grid through the distributed optical fiber sensor array to obtain original electrical parameter data; Performing wavelet packet transform analysis on the original electrical parameter data to obtain time-frequency electrical characteristic data; Using a preset tensor decomposition algorithm, a high-order singular value decomposition is performed on the time-frequency electrical characteristic data to obtain a key characteristic tensor; wherein the key characteristic tensor includes the time-frequency characteristics of power consumption fluctuation, the time-frequency distribution of power load and the time-frequency characteristics of power spectrum; The key feature tensor is subjected to dimensionality reduction processing to obtain power grid acquisition data.

[0008] Furthermore, the power grid collected data is predicted by a preset big data analysis technology to obtain the power grid operation trend, including: Performing data preprocessing on the power grid collected data to obtain preprocessed electrical data; Performing feature extraction on the preprocessed electrical data to obtain electrical extraction features; wherein the electrical extraction features; Performing time series data decomposition on the electrical extraction features to obtain multi-scale time series electrical features; wherein the multi-scale time series electrical features include load change features, voltage time domain features, current time domain features, power quality features, and power factor dynamic characteristics; Performing causal analysis on the electrical characteristics of the multi-scale time series by using a migration entropy algorithm to obtain a feature association network; wherein the feature association network is used to reveal the dynamic operation association between the electrical characteristics of each multi-scale time series in the target power grid; Using an adaptive kernel density estimation method, the state transition probability of the target power grid is estimated based on the feature association network to obtain a power grid state transition probability table; wherein the power grid state transition probability table is used to quantify the conversion probability of the target power grid between different working conditions, thereby performing a quantitative dynamic probability prediction of the future power grid operation state; Performing Markov chain prediction on the target power grid based on the power grid state transition probability table to obtain a power grid state prediction sequence; A self-organized criticality analysis method is used to perform nonlinear dynamic analysis on the grid state prediction sequence to obtain a grid operation trend; wherein the grid operation trend includes voltage stability, frequency stability, load change trend, and power quality change trend.

[0009] Furthermore, the energy state evaluation of the target energy storage system to obtain the available capacity of the target energy storage system includes: Real-time current data can be obtained by monitoring the target energy storage system through intelligent sensors; Using a segmented coulomb counter method, the accumulated charge of the target energy storage system is calculated based on the real-time current data to obtain an accumulated charge sequence; Performing multi-scale decomposition on the accumulated charge sequence by a multi-resolution analysis method to obtain a multi-scale accumulated charge feature; Dynamically adjusting the multi-scale cumulative charge characteristics using an adaptive filtering algorithm to obtain a cumulative charge estimation value; Performing storage voltage and storage charge relationship analysis on the target energy storage system based on the estimated value of the accumulated charge amount to obtain a storage voltage-storage charge characteristic curve; The rated capacity of the target energy storage system and the performance record of the target energy storage system are obtained from the database, and the available capacity of the target energy storage system is dynamically evaluated and reversely calculated through the storage voltage-storage charge characteristic curve, the rated capacity and the performance record to obtain the available capacity of the target energy storage system.

[0010] Further, based on the dynamic evaluation and reverse calculation of the available capacity of the target energy storage system through the storage voltage-storage charge characteristic curve, the rated capacity and the performance record, the available capacity of the target energy storage system is obtained, including: Using a piecewise exponential fitting algorithm to perform piecewise fitting processing on the storage voltage-storage charge characteristic curve to obtain a charge and discharge capacity loss characteristic parameter; Based on the charge and discharge capacity loss characteristic parameter, weighted statistics of the number of cycles of the target energy storage system are performed to obtain a capacity attenuation coefficient; Performing collaborative analysis on the target energy storage system based on the capacity attenuation coefficient and the performance record to obtain a health status of the target energy storage system; Based on the health status and the rated capacity, a dynamic capacity evaluation is performed on the current energy storage system to obtain a theoretical available capacity value; Acquiring a current temperature parameter through a preset temperature sensor, and performing capacity value compensation on the theoretical available capacity value based on the current temperature parameter to obtain a temperature corrected capacity value; Based on the temperature-corrected capacity value, the charge-discharge capacity loss characteristic parameter, the capacity attenuation coefficient and the health status, the available capacity of the target energy storage system is reversely calculated to obtain the available capacity of the target energy storage system.

[0011] Furthermore, based on the charge and discharge capacity loss characteristic parameter, weighted statistics of the number of cycles of the target energy storage system are performed to obtain a capacity attenuation coefficient, including: Performing time series analysis on the charge and discharge capacity loss characteristic parameters to obtain a charge and discharge cycle sequence; Based on the charge and discharge cycle sequence, counting the number of charge and discharge times of each cycle of the target energy storage system to obtain a cycle number data set; Performing weighted average calculation on the cycle number data set using a weighted moving average method to obtain a weighted cycle number; Based on the weighted number of cycles, a capacity loss of the energy storage system is fitted and analyzed to obtain a capacity decay curve; Performing slope analysis on the capacity decay curve to obtain a capacity decay rate; Based on the capacity decay rate and the weighted number of cycles, a capacity decay coefficient is calculated for the target energy storage system to obtain a capacity decay coefficient.

[0012] Furthermore, formulating a dispatching control plan based on the available capacity and the power grid operation trend includes: The available capacity and the grid operation trend are modeled in a state association manner through a dynamic programming algorithm to obtain available capacity-grid operation trend association data; wherein the available capacity-grid operation trend association data includes energy storage capacity state and load power deviation of the target grid; A preset fuzzy neural network is used to quantitatively evaluate the available capacity-grid operation trend correlation data to obtain a state evaluation result; wherein the state evaluation result includes a capacity margin parameter and a grid stability parameter; The state assessment result is monitored, and if any of the capacity margin parameter, the grid stability parameter, and the load balance parameter in the state assessment result is not within a preset range, a multi-objective weight allocation is performed on the state assessment result by using a hybrid particle swarm optimization algorithm to obtain a scheduling priority sequence; Performing scheduling instruction conversion on the scheduling priority sequence to obtain a preliminary scheduling instruction sequence; The preliminary scheduling instruction sequence is time-coordinated through a distributed collaborative optimization algorithm to obtain a scheduling control plan.

[0013] The present invention also provides an energy storage network control device, comprising: The acquisition module is used to collect real-time data of the target power grid through intelligent sensors to obtain power grid acquisition data; A prediction module, used to predict the collected data of the power grid by using a preset big data analysis technology to obtain the operation trend of the power grid; An evaluation module is used to evaluate the energy state of the target energy storage system and obtain the available capacity of the target energy storage system; A control module formulates a dispatching control plan based on the available capacity and the power grid operation trend; wherein the dispatching control plan is used to dispatch and control the target power grid or target energy storage system to keep the target power grid frequency stable.

[0014] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0015] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned methods are implemented.

[0016] The energy storage network control method provided by the present invention comprises the following steps: real-time data collection of the target power grid is performed through intelligent sensors to obtain power grid collection data; the power grid collection data is predicted through a preset big data analysis technology to obtain the power grid operation trend; the energy state of the target energy storage system is evaluated to obtain the available capacity of the target energy storage system; a dispatching control plan is formulated based on the available capacity and the power grid operation trend; wherein the dispatching control plan is used to dispatch and control the target power grid or the target energy storage system to keep the target power grid frequency stable. Through the above-mentioned technical means, the technical problem of how to effectively integrate the energy storage system into the existing power network and ensure that it can stably support the power grid frequency and other key performance indicators is solved, and the preset big data analysis technology is used to process the power grid collection data, which can provide a more accurate and reliable prediction of the power grid operation trend. Compared with traditional methods, this method can significantly improve the prediction accuracy and provide a more solid data basis for decision-making. At the same time, the real-time data collection and rapid data analysis capabilities also enhance the system's response speed to emergencies, enabling the energy storage control system to make the best response in the shortest time. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the steps of the energy storage network control method in one embodiment of the present invention; Figure 2 It is a structural block diagram of an energy storage network control device in one embodiment of the present invention; Figure 3 It is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0018] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0020] like Figure 1 As shown, Figure 1 It is a schematic diagram of the steps of a method for controlling energy storage network construction in one embodiment of the present invention; In one embodiment of the present invention, a method for controlling energy storage network construction is provided, comprising the following steps: Step S1, real-time data collection of the target power grid is performed through intelligent sensors to obtain power grid collection data.

[0021] Specifically, the step of collecting data from the target power grid in real time through smart sensors to obtain the data collected by the power grid is the basis of the entire energy storage network control method. In this process, smart sensors, as key tools for data collection, are deployed at various key nodes of the power system, which may include substations, transmission lines, distribution facilities, and user access points. Smart sensors can monitor and record various operating parameters of the power grid in real time, such as voltage, current, power factor, frequency, etc., to ensure that the data obtained is highly accurate and reliable. For example, in an urban power grid containing a large number of distributed energy resources (DERs), smart sensors can help capture fluctuations in solar panel power generation due to weather changes, or sudden changes in wind turbine output power. Smart sensors are not limited to the measurement of physical parameters, they can also be integrated with other devices and technologies to achieve more complex functions. For example, smart sensors can be combined with communication modules to transmit the collected data to the data analysis center or cloud platform in real time. This immediacy is crucial for timely adjustment of the working status of the energy storage system, because any delay may result in missing the best response time. In addition, in order to ensure the security and integrity of the data, smart sensors are usually equipped with encryption technology and error detection mechanisms to prevent data from being tampered with or lost during transmission. When the smart sensor completes the data collection of the target power grid, these data will form the so-called power grid collection data, which is the basis for all subsequent analysis and decision-making. In modern power systems, as more and more renewable energy sources are integrated into the power grid, the operating conditions of the power grid are becoming more and more complex, and traditional static data can no longer meet the needs. Therefore, the dynamic and continuous data stream provided by smart sensors has become an indispensable part. Through in-depth mining and analysis of these data, the future operation trend of the power grid can be predicted, the energy state of the energy storage system can be evaluated, and an effective dispatching control plan can be finally formulated. For example, in a regional power grid with a high proportion of wind power generation, the precise wind speed and wind direction data provided by smart sensors can help predict the wind power output in the future, so as to plan the charging and discharging strategy of the energy storage system in advance, and ensure that the stable power supply of the power grid can be maintained even in the case of insufficient wind power. In short, real-time data collection of the target power grid through smart sensors not only provides necessary information support for the energy storage network control method, but also largely determines the effect and efficiency of the entire control system. The application of smart sensors enables us to maintain accurate control of the power grid status in a more complex and changeable power environment, thereby achieving more intelligent and efficient power management.

[0022] Step S2, predicting the power grid collected data by using a preset big data analysis technology to obtain the power grid operation trend.

[0023] Specifically, the step of predicting the power grid collected data by using the preset big data analysis technology to obtain the power grid operation trend is a key link in the energy storage network control method. This process is immediately after the smart sensor completes the real-time data collection of the target power grid, and aims to use advanced data analysis methods to process and analyze the massive power grid collected data provided by the smart sensor. With the increasing complexity of the power system, especially when a large number of distributed energy resources (DERs) such as wind power and solar energy are connected to the power grid, traditional analysis methods have been unable to cope with such a large and dynamically changing data set. Therefore, the introduction of preset big data analysis technology has become an inevitable choice. The preset big data analysis technology covers a variety of algorithms and models, which are pre-selected and configured to efficiently process the data stream from the smart sensor. For example, in an urban power grid containing multiple wind farms, big data analysis technology can integrate time series analysis, machine learning algorithms, and deep learning models to mine potential patterns and correlations in the data. Specifically, time series analysis can help identify the trend of wind power output changing over time and seasons; machine learning algorithms can train models based on historical data to predict wind power output in a specific time period in the future; and deep learning models can more deeply understand complex nonlinear relationships and improve prediction accuracy. Through such comprehensive applications, the original power grid collection data can be effectively converted into forward-looking power grid operation trend predictions. In addition, the preset big data analysis technology is not limited to processing a single type of input data, it can also integrate multi-source information, including weather forecasts, user electricity behavior, market electricity price fluctuations and other external factors. For example, when predicting the load demand of a city's power grid, in addition to considering the production capacity of local power generation facilities, weather forecast data can also be combined to evaluate the changes in the frequency of air conditioning use, or refer to the impact of major historical events on electricity consumption. This multi-dimensional data fusion makes the prediction results more comprehensive and accurate, and provides a solid foundation for the subsequent formulation of reasonable dispatching and control plans. In order to ensure the reliability of the prediction results, the preset big data analysis technology will continue to optimize its own model parameters and self-correct through a feedback mechanism. This means that with the influx of more new data and the advancement of technology, the prediction model will gradually become more accurate. For example, in an actual application scenario, if a regional power grid experiences several unforeseen peak load conditions, the big data analysis system will automatically adjust its internal algorithm to learn the laws behind these anomalies, thereby making more accurate predictions in the future. Such adaptive capabilities are essential to maintaining grid stability, especially when facing rapidly changing renewable energy inputs, so that it can prepare in advance and avoid frequency fluctuations caused by imbalances in supply and demand. In summary, predicting the data collected by the power grid through the preset big data analysis technology not only improves the understanding and grasp of the power grid operation trend, but also provides a scientific basis for the effective management and scheduling of energy storage systems.The application of this method enables the power system to maintain high flexibility and responsiveness in a complex and changing environment, ultimately achieving more intelligent and efficient energy management.

[0024] Step S3: Evaluate the energy status of the target energy storage system to obtain the available capacity of the target energy storage system.

[0025] Specifically, the energy state evaluation of the target energy storage system and the available capacity of the target energy storage system are key steps to ensure the effective implementation of the energy storage network control method. This process is immediately followed by the completion of grid data collection through intelligent sensors and the prediction of grid operation trends using preset big data analysis technology, aiming to accurately understand the current charge and discharge state of the energy storage system and its actual energy support capacity. To achieve this, a series of advanced monitoring and evaluation technologies are required, which work together to provide a comprehensive and accurate energy state report. In specific operations, energy state evaluation first involves monitoring the internal parameters of the energy storage system, including but not limited to battery voltage, current, temperature, etc. These parameters not only reflect the immediate working status of the energy storage device, but also provide important clues about its health status. For example, in a city power grid containing multiple lithium battery energy storage devices, by real-time monitoring of the voltage level of each battery cell, it can be determined which batteries are close to full charge and which may be in a low power or even faulty state. In addition, temperature information is equally important for evaluating battery life and safety. Excessive temperature may lead to performance degradation or safety hazards, so it must be taken into consideration. Furthermore, in order to understand the overall energy state of the energy storage system from a more macro perspective, it is also necessary to conduct a comprehensive analysis in combination with historical operating data and current grid demand. This means that not only the technical characteristics of the energy storage system itself should be considered, but also it should be examined in the context of the entire power system. For example, in a regional power grid with a high proportion of wind power generation, when the upcoming wind speed reduction is predicted to lead to a reduction in wind power output, the energy storage system plays the role of supplementary energy. At this time, the energy state assessment will take into account the charging and discharging records of the energy storage system in the past few hours, combined with the expected load demand in the next few hours, calculate the total energy available for dispatch, and determine the best charging and discharging strategy to maintain grid frequency stability. In addition to direct physical parameters and technical indicators, energy state assessment can also introduce economic factors as an auxiliary decision-making basis. For example, considering the price fluctuations in the power market in different time periods, the energy storage system can be charged first when the electricity price is low, and release the stored energy to earn the difference when the electricity price is high. Such a strategy can not only improve the economic benefits of the energy storage system, but also enhance its support for the power grid. At the same time, this flexibility also enables the energy storage system to respond quickly to emergencies, such as sudden load peaks or insufficient renewable energy generation. Ultimately, by conducting a comprehensive energy status assessment of the target energy storage system, we can obtain a result that accurately reflects its available capacity. This is not only the basis for formulating a reasonable dispatch control plan, but also provides a scientific basis for optimizing the efficiency of energy storage resources. For example, in the aforementioned application scenario, if the assessment results show that the available capacity of a certain energy storage facility is insufficient to cope with the upcoming peak in electricity demand, then other energy storage systems or traditional power generation sources can be arranged in advance to ensure the continuity and stability of power supply.In short, this evaluation process is crucial to achieving efficient management of energy storage systems and stable operation of the power grid.

[0026] Step S4, formulating a dispatching control plan based on the available capacity and the grid operation trend; wherein the dispatching control plan is used to dispatch and control the target grid or target energy storage system to keep the target grid frequency stable.

[0027] Specifically, the process of formulating a dispatching control plan based on the available capacity and the power grid operation trend is a core component of the energy storage network control method. This process is based on the previous energy state evaluation of the target energy storage system and the prediction of the power grid operation trend through the preset big data analysis technology. It aims to formulate a set of dispatching control plans that can effectively maintain the stability of the target power grid frequency by comprehensively considering the current capacity of the energy storage system and the future demand changes of the power grid. First of all, in order to formulate such a dispatching control plan, the available capacity information obtained from the energy state evaluation of the energy storage system must be combined with the power grid operation trend predicted by the big data analysis technology. This means that the maximum energy output or absorption capacity that the energy storage system can provide in different time periods must be deeply analyzed and matched with the expected power supply and demand. For example, in an urban power grid containing multiple wind farms, when it is predicted that the wind speed will decrease in the next few hours, resulting in a decrease in wind power output, the dispatching control system needs to plan in advance how to use the energy storage system to make up for the possible energy gap. At this time, the available capacity data of the energy storage system provides key information input to help determine which energy storage devices can be put into use when to ensure that the stability of the power grid frequency is not affected. Furthermore, the development of a dispatch control plan is not just a simple arrangement of charging and discharging instructions. It also involves the application of complex decision-making algorithms and optimization models. These tools can simulate different dispatch scenarios, evaluate the effects of various schemes, and select the optimal solution. For example, the dispatch control system may combine multiple factors such as real-time electricity prices, user load demand, and weather forecasts to calculate the best strategy that can both meet immediate power demand and maximize economic benefits. At the same time, considering the safety and reliability requirements of the power grid, the plan will also set a series of constraints, such as avoiding excessive use of energy storage equipment and maintaining a certain amount of spare capacity to prevent the risk of power supply interruption due to emergencies. In addition, in order to ensure the dynamic adaptability of the dispatch control plan, the system also needs to have the ability to adjust in real time. This is because the operating environment of the power system is highly dynamic and not fully predictable, and even the most accurate prediction model may have errors. Therefore, once the actual operation deviates from expectations, such as an unexpected peak load suddenly appears in a certain area or a certain energy storage facility fails, the dispatch control system should be able to respond quickly and re-optimize the dispatch plan. For example, in the application scenario of the aforementioned urban power grid, if the actual power consumption in a certain period of time far exceeds expectations, the dispatching and control system will immediately activate additional energy storage resources, and even coordinate power support from neighboring areas to quickly restore the grid frequency to a normal range. Ultimately, through the dispatching and control plan formulated based on the available capacity and the grid operation trend, not only can the efficient dispatching and precise control of the target grid or energy storage system be achieved, but also a strong guarantee can be provided for the stable operation of the entire power system.The implementation of this plan enables the energy storage system to play a role when it is most needed, whether it is to deal with the intermittent problem of renewable energy generation or to handle sudden load fluctuations, it can effectively maintain the stability of the grid frequency. At the same time, this also promotes the flexibility and economy of the power market, and lays a solid foundation for building a more intelligent and sustainable modern power grid system.

[0028] In a specific embodiment, the smart sensor is a distributed optical fiber sensor array, and the real-time data collection of the target power grid by the smart sensor to obtain the power grid collection data includes: Sampling key nodes of the target power grid through the distributed optical fiber sensor array to obtain original electrical parameter data; Performing wavelet packet transform analysis on the original electrical parameter data to obtain time-frequency electrical characteristic data; Using a preset tensor decomposition algorithm, a high-order singular value decomposition is performed on the time-frequency electrical characteristic data to obtain a key characteristic tensor; wherein the key characteristic tensor includes the time-frequency characteristics of power consumption fluctuation, the time-frequency distribution of power load and the time-frequency characteristics of power spectrum; The key feature tensor is subjected to dimensionality reduction processing to obtain power grid acquisition data.

[0029] Specifically, the smart sensor is a distributed optical fiber sensor array. The process of acquiring the data collected from the target power grid in real time through this specific type of smart sensor is the first crucial step in the entire energy storage network control method. This process not only involves the use of advanced sensing technology to obtain the original electrical parameter data, but also includes complex data processing steps, such as wavelet packet transform analysis, application of tensor decomposition algorithm and dimensionality reduction processing, so as to finally obtain the power grid collection data for subsequent analysis and decision-making. First of all, as a highly sensitive and widely distributed monitoring tool, the distributed optical fiber sensor array is deployed at the key nodes of the target power grid. These key nodes may include substations, transmission lines, distribution facilities and user access points. The distributed optical fiber sensor array can reflect the changes in the surrounding environment by accurately measuring the propagation characteristics of the optical signal, thereby realizing the monitoring of the internal state of the power system. For example, in an urban power grid containing multiple wind farms, the distributed optical fiber sensor array can help capture the fluctuations in the power generation of solar panels due to weather changes, or the sudden changes in the output power of wind turbines. Through such a sampling method, high-resolution original electrical parameter data covering a wide area can be obtained, providing a solid foundation for subsequent in-depth analysis. Next, in order to extract valuable information from a large amount of raw electrical parameter data, wavelet packet transform analysis is required. Wavelet packet transform is a multi-resolution analysis tool that can decompose signals in the time domain into components in different frequency bands, thereby revealing the time-frequency characteristics of the signal. For the power grid, this means that the fluctuations in power consumption, load distribution, and the change patterns of power spectrum over time and frequency can be more clearly understood. For example, in the application scenario of the aforementioned urban power grid, when the wind speed suddenly increases or decreases, wavelet packet transform analysis can help identify the time periods in which wind power output has changed significantly, and how these changes affect the overall operation of the power grid. In addition, it can also detect changes in user-side load patterns, such as the difference between peak hours during the day and low hours at night in commercial areas, or different electricity consumption behaviors on weekends and weekdays in residential areas. Then, the preset tensor decomposition algorithm is used to perform high-order singular value decomposition on the time-frequency electrical feature data obtained after wavelet packet transform to obtain the key feature tensor. The key feature tensor here contains three main aspects of information: the time-frequency characteristics of power consumption fluctuations, the time-frequency distribution of power loads, and the time-frequency characteristics of power spectrum. Tensor decomposition algorithms can effectively extract core structures from multi-dimensional data sets, making originally complex data more intuitive and easy to understand.For example, when analyzing the long-term operation trend of a regional power grid, the future power demand can be predicted by observing the time-frequency characteristics of power consumption fluctuations; the time-frequency distribution of power load can be used to optimize the dispatching strategy to ensure sufficient spare capacity during peak periods; and the time-frequency characteristics of the power spectrum can help evaluate the quality of the power grid, especially when facing intermittent input of renewable energy, and can be adjusted in time to maintain a stable power supply frequency. Finally, in order to make the obtained key feature tensor suitable for fast processing and efficient storage in practical applications, it is also necessary to perform dimensionality reduction processing on it. The purpose of dimensionality reduction processing is to remove redundant information, retain the core features that best represent the state of the power grid, and minimize the consumption of computing resources. In this process, principal component analysis (PCA), linear discriminant analysis (LDA) or other appropriate dimensionality reduction techniques can be used. For example, in the example of the aforementioned urban power grid, the power grid collection data after dimensionality reduction processing can be used to train machine learning models, predict the power grid operation trend in the future, and formulate a reasonable energy storage dispatching plan. This can not only improve the prediction accuracy, but also speed up the response speed, ensuring that the optimal response can be made quickly even in a complex power environment to maintain the stability of the power grid frequency. In summary, by sampling the key nodes of the target power grid through a distributed fiber optic sensor array, the original electrical parameter data is obtained, and then the data is further analyzed by wavelet packet transform and high-order singular value decomposition using tensor decomposition algorithm, and finally the dimensionality reduction processing is completed. This series of steps constitutes a complete process from raw data to power grid acquisition data that can be used for decision support. This method not only improves the accuracy and breadth of data acquisition, but also mines deep-level information about power grid operation through advanced data analysis methods, providing strong technical support for the effective management of energy storage systems and the stable operation of power grids. In the context of the increasing complexity of modern power systems, such data processing and analysis capabilities are particularly important. It enables us to maintain accurate control of the power grid status in a more dynamic and uncertain environment, thereby achieving more intelligent and efficient energy management.

[0030] In a specific embodiment, the predicting of the power grid collected data by a preset big data analysis technology to obtain the power grid operation trend includes: Performing data preprocessing on the power grid collected data to obtain preprocessed electrical data; Performing feature extraction on the preprocessed electrical data to obtain electrical extraction features; wherein the electrical extraction features; Performing time series data decomposition on the electrical extraction features to obtain multi-scale time series electrical features; wherein the multi-scale time series electrical features include load change features, voltage time domain features, current time domain features, power quality features, and power factor dynamic characteristics; Performing causal analysis on the electrical characteristics of the multi-scale time series by using a migration entropy algorithm to obtain a feature association network; wherein the feature association network is used to reveal the dynamic operation association between the electrical characteristics of each multi-scale time series in the target power grid; Using an adaptive kernel density estimation method, the state transition probability of the target power grid is estimated based on the feature association network to obtain a power grid state transition probability table; wherein the power grid state transition probability table is used to quantify the conversion probability of the target power grid between different working conditions, thereby performing a quantitative dynamic probability prediction of the future power grid operation state; Performing Markov chain prediction on the target power grid based on the power grid state transition probability table to obtain a power grid state prediction sequence; A self-organized criticality analysis method is used to perform nonlinear dynamic analysis on the grid state prediction sequence to obtain a grid operation trend; wherein the grid operation trend includes voltage stability, frequency stability, load change trend, and power quality change trend.

[0031] Specifically, the process of predicting the power grid collected data by the preset big data analysis technology to obtain the power grid operation trend is one of the core links in the energy storage network control method. This process not only involves the preliminary processing and feature extraction of the power grid collected data, but also deeply applies a variety of advanced technical means such as time series data analysis, causal relationship analysis, state transition probability estimation and nonlinear dynamic analysis, and finally realizes the quantitative prediction of the future power grid operation state. The following is a specific explanation of this complex process. First, for the original power grid collected data obtained from the distributed optical fiber sensor array, data preprocessing must be performed to obtain preprocessed electrical data. The data preprocessing step is to clean and standardize these raw data to ensure their quality and consistency. For example, in an urban power grid containing multiple wind farms, the collected data may have missing values ​​or outliers because the sensors may be affected by environmental noise or equipment failure. In order to ensure the effectiveness of subsequent analysis, it is necessary to use interpolation to fill the missing data, and identify and eliminate outliers through statistical tests. In addition, considering that the measurement units and timestamps between different sensors may be different, unified conversion and synchronization processing are also required. Such preprocessing work lays a solid foundation for subsequent feature extraction. Next, feature extraction is performed on the preprocessed electrical data to obtain electrical extraction features. The goal of this step is to extract the most representative and distinguishing features from a large amount of preprocessed electrical data in order to better describe the state of the power grid. Feature extraction can be based on traditional statistical methods, such as mean, variance, etc., or can be combined with modern machine learning algorithms, such as principal component analysis (PCA), independent component analysis (ICA), etc. For example, in the application scenario of the aforementioned urban power grid, feature extraction can help identify which time periods have the most drastic fluctuations in power consumption, or which areas have the most unstable voltage levels. In this way, the dynamic change pattern within the power grid can be more clearly understood, providing support for further in-depth analysis. Subsequently, the electrical extraction features are subjected to time series data decomposition to obtain multi-scale time series electrical features. The multi-scale time series electrical features here include load change features, voltage time domain features, current time domain features, power quality features, and power factor dynamic characteristics. Time series data decomposition techniques, such as empirical mode decomposition (EMD) and wavelet transform, can decompose complex time series data into different frequency components and reveal the laws of change at all levels. For example, when analyzing the output power of a wind farm, long-term trend changes caused by weather conditions and short-term rapid changes caused by instantaneous fluctuations in wind speed can be identified through time series data decomposition. This multi-scale perspective helps to fully understand the operation status of the power grid and provides a rich source of information for subsequent causal analysis. Then, the multi-scale time series electrical characteristics are analyzed for causality through the migration entropy algorithm to obtain a feature association network.Migration entropy is a method to measure the causal relationship between variables, which can reveal the mutual influence and transmission path between different electrical characteristics. The feature association network is used to intuitively display these causal relationships, helping us understand the dynamic operation associations between the electrical characteristics of each multi-scale time series in the target power grid. For example, in the above-mentioned example of the urban power grid, by constructing a feature association network, we can find out how certain specific load changes cause changes in voltage levels, or how the adjustment of power factor affects the overall power quality. Such analysis not only deepens the understanding of the internal mechanism of the power grid, but also provides a theoretical basis for formulating effective control strategies. Further, the state transition probability of the target power grid is estimated based on the feature association network using the adaptive kernel density estimation method to obtain a power grid state transition probability table. Adaptive kernel density estimation is a non-parametric probability density estimation method that can flexibly adapt to different types of data distributions, thereby accurately quantifying the conversion probability of the target power grid between different working conditions. For example, in a practical application scenario, if the wind power output increases significantly over a period of time, the power grid state transition probability table can tell us how likely other key indicators (such as voltage stability, frequency stability, etc.) will change accordingly in this case. Such probability estimation provides a quantitative dynamic prediction for the future operation state of the power grid, so that we can prepare in advance and take necessary precautions. Finally, the target power grid is predicted by Markov chain based on the power grid state transition probability table to obtain a power grid state prediction sequence. The Markov chain prediction model assumes that the future state of the system depends only on the current state and is not affected by the past state. In this way, a series of possible power grid state evolution paths, namely, power grid state prediction sequences, can be generated. For example, in the application scenario of the aforementioned urban power grid, Markov chain prediction can help us simulate different state combinations that the power grid may experience in the next few hours, including possible peak load periods, voltage drop risks, etc. Such prediction results are crucial for optimizing energy storage scheduling plans because they provide us with clear guidance on when and where action needs to be taken. On this basis, the self-organized criticality analysis method is used to perform nonlinear dynamic analysis on the power grid state prediction sequence to obtain the power grid operation trend. Self-organized criticality analysis focuses on the behavior of the system when it approaches the critical point, and is particularly suitable for describing networks with complex dynamic characteristics such as power systems. In this way, the ability of the power grid to maintain stability under different load conditions can be evaluated, and the trend of voltage stability, frequency stability, load change trend and power quality change trend can be predicted. For example, when faced with sudden high load demands, self-organized criticality analysis can help determine whether the power grid is within a safe range or whether there is a potential risk of instability. Such analysis results not only improve our understanding of power grid behavior, but also enhance our ability to respond to emergencies and ensure safe and reliable power supply.In summary, the process of predicting the power grid collected data by using the preset big data analysis technology to obtain the power grid operation trend not only demonstrates the complete chain from data to knowledge to decision support, but also reflects the advanced concepts and technical means in modern power system management and optimization. In this process, each step is closely linked and together constitutes a powerful prediction framework, which allows us to more accurately predict the future operation status of the power grid and make reasonable control decisions in time to maintain the stability of the power grid frequency and the quality of power. The application of this method is of great significance to promoting the efficient use of clean energy and improving the flexibility and reliability of the power system.

[0032] In a specific embodiment, the step of evaluating the energy state of the target energy storage system to obtain the available capacity of the target energy storage system includes: Real-time current data can be obtained by monitoring the target energy storage system through intelligent sensors; Using a segmented coulomb counter method, the accumulated charge of the target energy storage system is calculated based on the real-time current data to obtain an accumulated charge sequence; Performing multi-scale decomposition on the accumulated charge sequence by a multi-resolution analysis method to obtain a multi-scale accumulated charge feature; Dynamically adjusting the multi-scale cumulative charge characteristics using an adaptive filtering algorithm to obtain a cumulative charge estimation value; Performing storage voltage and storage charge relationship analysis on the target energy storage system based on the estimated value of the accumulated charge amount to obtain a storage voltage-storage charge characteristic curve; The rated capacity of the target energy storage system and the performance record of the target energy storage system are obtained from the database, and the available capacity of the target energy storage system is dynamically evaluated and reversely calculated through the storage voltage-storage charge characteristic curve, the rated capacity and the performance record to obtain the available capacity of the target energy storage system.

[0033] Specifically, the process of evaluating the energy state of the target energy storage system and obtaining the available capacity of the target energy storage system is one of the key steps to ensure the effective implementation of the energy storage network control method. This process not only involves obtaining real-time current data through intelligent sensors, but also covers multiple complex technical links from cumulative charge calculation to multi-scale decomposition, dynamic adjustment and final available capacity evaluation. The following is a specific explanation of this detailed scheme. First, the target energy storage system is monitored in real time by intelligent sensors to obtain real-time current data. Intelligent sensors are deployed at various key locations of the energy storage system and can continuously capture current changes flowing through the energy storage device. For example, in an urban power grid containing multiple lithium battery energy storage units, these sensors can be installed on the input and output ports of each battery module to ensure that high-precision and high-frequency current measurements are obtained. Real-time current data is not only the basis for understanding the current working state of the energy storage system, but also provides important original information for subsequent energy state evaluation. Next, the segmented coulomb counter method is used to calculate the cumulative charge of the target energy storage system based on the real-time current data to obtain a cumulative charge sequence. The segmented coulomb counter method is a method for accurately calculating the accumulated charge during the charging and discharging process. It divides the entire charging and discharging cycle into several small time periods and calculates the net charge inflow or outflow in each time period. This method is particularly suitable for processing nonlinear charging curves and complex current waveforms. For example, in the aforementioned application scenario of the urban power grid, when a certain energy storage unit undergoes a period of rapid charging and then enters a slow discharge phase, the segmented coulomb counter method can accurately track the changes in the accumulated charge through meticulous time segmentation and generate a detailed accumulated charge sequence. This provides a reliable data basis for subsequent analysis. Then, the accumulated charge sequence is multi-scale decomposed by a multi-resolution analysis method to obtain a multi-scale accumulated charge feature. Multi-resolution analysis methods such as wavelet transform can decompose the accumulated charge sequence into components in different frequency ranges, thereby revealing the charge accumulation characteristics on different time scales. For example, for a long-term energy storage system, there may be daily periodic load fluctuations and seasonal changes in power generation patterns, while on a short time scale there is the influence of instantaneous power mutations. By performing multi-scale decomposition on the cumulative charge sequence, we can identify the changing patterns at these different levels, and thus better understand the usage history of the energy storage system and its impact on the current state. Furthermore, an adaptive filtering algorithm is used to dynamically adjust the multi-scale cumulative charge characteristics to obtain an estimated value of the cumulative charge. The adaptive filtering algorithm can automatically adjust parameters according to changes in the cumulative charge characteristics to eliminate noise interference and improve estimation accuracy. For example, in practical applications, measurement errors caused by changes in ambient temperature or other factors may cause abnormal fluctuations in the cumulative charge sequence.At this time, the adaptive filtering algorithm can correct these errors in real time according to the historical trend and current changes of the cumulative charge characteristics, and provide a more accurate cumulative charge estimate. Such dynamic adjustment helps to maintain the consistency and reliability of the energy storage system evaluation results. On this basis, the target energy storage system is analyzed for the storage voltage and storage charge relationship based on the cumulative charge estimate to obtain a storage voltage-storage charge characteristic curve. This characteristic curve describes the relationship between the internal charge storage and the external performance voltage of the energy storage system, reflecting its physical properties and working state. For example, in a lithium battery energy storage system, the terminal voltage of the battery will change accordingly with the different degree of charge and discharge, forming a specific voltage-charge curve. By comparing the cumulative charge estimate with the actual measured storage voltage, a characteristic curve that accurately reflects the current health of the energy storage system can be drawn, which is crucial for evaluating its remaining power and predicting future performance. Finally, the rated capacity of the target energy storage system and the performance record of the target energy storage system are obtained from the database, and the available capacity of the target energy storage system is dynamically evaluated and reversely calculated through the storage voltage-storage charge characteristic curve, the rated capacity and the performance record to obtain the available capacity of the target energy storage system. The database stores detailed information about the energy storage system, including the rated capacity at the time of leaving the factory, previous maintenance records, and historical operation data. Combined with the above characteristic curves, the current available capacity of the energy storage system can be dynamically evaluated. For example, in the above-mentioned example of the urban power grid, if a certain energy storage unit has been frequently charged and discharged for a period of time, its actual available capacity may be lower than the initial rated value. By comprehensively analyzing the characteristic curve, rated capacity, and performance records, the current real available capacity of the energy storage unit can be accurately calculated so as to reasonably arrange its participation in the grid dispatching task. In summary, the process of evaluating the energy state of the target energy storage system and obtaining the available capacity of the target energy storage system not only demonstrates the complete chain from real-time data collection to advanced data analysis, but also reflects the advanced concepts and technical means in modern energy storage management systems. In this process, each technical link is closely linked to form a powerful evaluation framework, which enables us to more accurately grasp the actual state of the energy storage system and make reasonable dispatching decisions in time to maintain the stability of the grid frequency and the good power quality. The application of this method is of great significance for optimizing the utilization efficiency of energy storage resources and improving the flexibility and reliability of the power system.

[0034] In a specific embodiment, based on the dynamic evaluation and reverse calculation of the available capacity of the target energy storage system through the storage voltage-storage charge characteristic curve, the rated capacity and the performance record, the available capacity of the target energy storage system is obtained, including: Using a piecewise exponential fitting algorithm to perform piecewise fitting processing on the storage voltage-storage charge characteristic curve to obtain a charge and discharge capacity loss characteristic parameter; Based on the charge and discharge capacity loss characteristic parameter, weighted statistics of the number of cycles of the target energy storage system are performed to obtain a capacity attenuation coefficient; Performing collaborative analysis on the target energy storage system based on the capacity attenuation coefficient and the performance record to obtain a health status of the target energy storage system; Based on the health status and the rated capacity, a dynamic capacity evaluation is performed on the current energy storage system to obtain a theoretical available capacity value; Acquiring a current temperature parameter through a preset temperature sensor, and performing capacity value compensation on the theoretical available capacity value based on the current temperature parameter to obtain a temperature corrected capacity value; Based on the temperature-corrected capacity value, the charge-discharge capacity loss characteristic parameter, the capacity attenuation coefficient and the health status, the available capacity of the target energy storage system is reversely calculated to obtain the available capacity of the target energy storage system.

[0035] Specifically, the process of obtaining the available capacity of the target energy storage system based on the dynamic evaluation and reverse calculation of the available capacity of the target energy storage system through the storage voltage-storage charge characteristic curve, rated capacity and performance record is a key link to ensure efficient management and optimized scheduling of the energy storage system. This process not only involves the fine processing of the storage voltage-storage charge characteristic curve, but also includes the extraction of the characteristic parameters of charge and discharge capacity loss, the statistics of the capacity attenuation coefficient, the coordinated analysis of the health status, the dynamic evaluation of the theoretical available capacity value, and the compensation of the temperature-corrected capacity value. The following is a specific explanation of this complex scheme. First, the storage voltage-storage charge characteristic curve is segmented and fitted by a segmented exponential fitting algorithm to obtain the characteristic parameters of charge and discharge capacity loss. Segmented exponential fitting is a mathematical modeling method that divides the characteristic curve into several linear or nonlinear parts and fits an exponential function to each part. This method is particularly suitable for describing the nonlinear behavior of energy storage systems under different charging states. For example, in an urban power grid containing multiple lithium battery energy storage units, as the number of charge and discharge cycles of the battery increases, its internal chemical reaction may cause the capacity to gradually decrease. Through piecewise exponential fitting, we can identify the trend of these changes and quantify the degree of capacity loss in each stage, thereby obtaining the characteristic parameters of charge and discharge capacity loss. This not only helps to understand the current state of the energy storage system, but also provides a scientific basis for subsequent capacity evaluation. Next, based on the characteristic parameters of charge and discharge capacity loss, the target energy storage system is subjected to weighted statistics of the number of cycles to obtain a capacity attenuation coefficient. The weighted statistics of the number of cycles here refers to assigning different weights to reflect the impact of each cycle on the total capacity based on the historical operation data of the energy storage system, especially the number of charge and discharge cycles. For example, in the aforementioned application scenario of the urban power grid, if a certain energy storage unit has experienced a large number of rapid charge and discharge operations in the past year, then these high-frequency short-cycle cycles may have a greater impact on the total capacity than low-frequency long-cycle cycles. By weighted statistics of these data, the capacity attenuation coefficient can be accurately calculated, which directly reflects the capacity loss of the energy storage system due to long-term use. Further, the target energy storage system is collaboratively analyzed based on the capacity attenuation coefficient and the performance record to obtain the health status of the target energy storage system. The collaborative analysis mentioned here is not just a simple data summary, but a comprehensive consideration of the capacity attenuation coefficient and the energy storage system's previous maintenance records, fault reports and other information. For example, in actual applications, in addition to the number of charge and discharge cycles, environmental conditions (such as temperature, humidity), external load requirements and other factors will also affect the life of the energy storage system. Through collaborative analysis, the health status of the energy storage system can be fully evaluated to determine whether it is within the normal working range, or whether there are potential risks that need to be dealt with in a timely manner.Such analysis results are of great significance for formulating reasonable maintenance plans and extending the service life of energy storage systems. On this basis, based on the health status and the rated capacity, the current energy storage system is dynamically evaluated to obtain the theoretical available capacity value. Dynamic capacity evaluation is a continuously updated process, which combines the latest health status evaluation results and the initial rated capacity of the energy storage system to provide a value that reflects the current energy storage capacity of the energy storage system in real time. For example, in the aforementioned example of the urban power grid, if the health status of a certain energy storage unit has declined after a period of frequent use, then even if its rated capacity has not changed, its theoretical available capacity value should be adjusted down accordingly. This dynamic evaluation can help us more accurately grasp the actual state of the energy storage system so as to reasonably arrange its tasks of participating in power grid dispatching. In addition, in order to improve the accuracy of the evaluation results, it is also necessary to obtain the current temperature parameter through a preset temperature sensor, and compensate the theoretical available capacity value based on the current temperature parameter to obtain the temperature corrected capacity value. Temperature is one of the important factors affecting the performance of the energy storage system. Especially under extreme conditions, temperature changes may cause changes in the chemical reaction rate inside the battery, thereby affecting its charging and discharging efficiency and capacity. For example, in cold winter weather, the charging and discharging performance of lithium batteries is usually suppressed, so it is necessary to appropriately adjust the theoretical available capacity value according to the actual measured temperature parameters to obtain a more realistic temperature-corrected capacity value. Such compensation measures ensure the consistency and reliability of the evaluation results under different environmental conditions. Finally, based on the temperature-corrected capacity value, the characteristic parameters of the charge and discharge capacity loss, the capacity attenuation coefficient and the health status, the available capacity of the target energy storage system is reversely calculated to obtain the available capacity of the target energy storage system. Reverse calculation refers to combining all the above evaluation results to reversely infer the maximum available capacity that the energy storage system can provide in the current state. For example, in the application scenario of the aforementioned urban power grid, when facing the upcoming peak power consumption, reverse calculation can be used to determine which energy storage units can provide sufficient power support at critical moments, and which may not be able to meet the demand due to poor health or other reasons. This precise available capacity assessment not only helps to optimize the scheduling of energy storage resources, but also effectively prevents the problem of insufficient power supply caused by misjudgment. In summary, the process of dynamically evaluating and reversely calculating the available capacity of the target energy storage system based on the storage voltage-storage charge characteristic curve, rated capacity and performance record not only demonstrates the complete chain from characteristic curve modeling to capacity evaluation, but also reflects the advanced concepts and technical means in modern energy storage management systems. In this process, each technical link is closely linked to form a powerful evaluation framework, which enables us to more accurately grasp the actual status of the energy storage system and make reasonable scheduling decisions in a timely manner to maintain stable grid frequency and good power quality.The application of this method is of great significance for optimizing the utilization efficiency of energy storage resources and improving the flexibility and reliability of power systems.

[0036] In a specific embodiment, based on the charge and discharge capacity loss characteristic parameter, weighted statistics of the number of cycles of the target energy storage system are performed to obtain a capacity attenuation coefficient, including: Performing time series analysis on the charge and discharge capacity loss characteristic parameters to obtain a charge and discharge cycle sequence; Based on the charge and discharge cycle sequence, counting the number of charge and discharge times of each cycle of the target energy storage system to obtain a cycle number data set; Performing weighted average calculation on the cycle number data set using a weighted moving average method to obtain a weighted cycle number; Based on the weighted number of cycles, a capacity loss of the energy storage system is fitted and analyzed to obtain a capacity decay curve; Performing slope analysis on the capacity decay curve to obtain a capacity decay rate; Based on the capacity decay rate and the weighted number of cycles, a capacity decay coefficient is calculated for the target energy storage system to obtain a capacity decay coefficient.

[0037] Specifically, based on the characteristic parameters of charge and discharge capacity loss, the process of performing weighted statistics on the number of cycles of the target energy storage system to obtain the capacity attenuation coefficient is one of the key steps in the energy storage network control method for evaluating the health status of the energy storage system and predicting its future performance. This process not only involves time series analysis of the characteristic parameters of charge and discharge capacity loss, but also includes statistics of the cycle number data set, weighted average calculation, fitting analysis, and final capacity attenuation coefficient calculation. The following is a specific explanation of this complex scheme. First, the characteristic parameters of charge and discharge capacity loss are analyzed in time series to obtain a charge and discharge cycle sequence. Time series analysis is a technique for identifying and quantifying data patterns that change over time. Here, by analyzing the time series of the characteristic parameters of charge and discharge capacity loss, the charge and discharge behavior of the energy storage system in different time periods and its impact on capacity can be captured. For example, in an urban power grid containing multiple lithium battery energy storage units, these characteristic parameters record the changes in battery capacity after each charge and discharge operation. Through time series analysis, we can connect these discrete data points to form a continuous charge and discharge cycle sequence, which intuitively shows the historical operation trajectory of the energy storage system and provides a basis for subsequent statistical work. Next, based on the charge and discharge cycle sequence, the number of charges and discharges in each cycle of the target energy storage system is counted to obtain a cycle number data set. The number of cycles here refers to the number of times the energy storage system completes a complete charge and discharge process. Counting the number of charges and discharges in each cycle helps us understand the frequency and intensity of use of the energy storage system. For example, in the aforementioned application scenario of the urban power grid, if a certain energy storage unit has experienced a large number of rapid charge and discharge operations in the past year, these high-frequency short-cycle cycles may have a greater impact on the total capacity than low-frequency long-cycle cycles. By constructing a cycle number data set, the working history of the energy storage system can be fully grasped, and detailed input data can be provided for subsequent weighted average calculations. Further, the weighted moving average method is used to perform a weighted average calculation on the cycle number data set to obtain a weighted cycle number. The weighted moving average method is a commonly used smoothing technique that assigns different weights to data in different time periods, thereby reducing the impact of short-term fluctuations and highlighting long-term trends. In energy storage system evaluation, this method is particularly suitable for dealing with non-uniform charging and discharging behaviors caused by changes in environmental conditions or load demand. For example, in actual applications, charging and discharging activities may be more frequent in certain periods, while relatively less frequent in other periods. Through the weighted moving average method, appropriate weights can be assigned to each cycle according to its importance and impact, and then a weighted number of cycles that can reflect the overall use of the energy storage system can be calculated. Such calculation results are not only more accurate, but also more representative. On this basis, based on the weighted number of cycles, the capacity loss of the energy storage system is fitted and analyzed to obtain the capacity decay curve.Fitting analysis aims to find the best mathematical model to describe the trend that the capacity of the energy storage system gradually decreases as the number of cycles increases. For example, linear regression, exponential function or other suitable models can be used to fit the relationship between the weighted number of cycles and capacity loss. The capacity decay curve not only intuitively shows the life characteristics of the energy storage system, but also provides an important reference for predicting future capacity changes. By comparing the capacity decay curves of different time periods, the performance of the energy storage system under different usage conditions can be evaluated, and a basis for optimizing its operation strategy can be provided. Then, the slope analysis of the capacity decay curve is performed to obtain the capacity decay rate. Slope analysis is a method of quantitatively describing the capacity decay curve, which reflects the rate of capacity loss of the energy storage system after a certain number of cycles. For example, in the above-mentioned example of the urban power grid, if the capacity decay curve of a certain energy storage unit shows a steeper slope, this indicates that the unit has a faster capacity loss rate and may require more frequent maintenance or replacement. On the contrary, if the slope is relatively gentle, it means that the unit has good durability and can maintain a high performance level for a longer period of time. Through slope analysis, the aging rate of the energy storage system can be quantified and a scientific basis can be provided for formulating a reasonable maintenance plan. Finally, based on the capacity decay rate and the weighted number of cycles, the capacity decay coefficient of the target energy storage system is calculated to obtain the capacity decay coefficient. The capacity decay coefficient is a comprehensive indicator that combines the results of the slope analysis and the information of the weighted number of cycles to characterize the capacity change characteristics of the energy storage system throughout its life cycle. For example, in actual application scenarios, if a certain energy storage unit has undergone a large number of charge and discharge cycles, and its capacity decay curve shows a significant downward trend, then by calculating the capacity decay coefficient, we can derive a specific value to measure the current health status and remaining service life of the unit. This accurate evaluation result is crucial for optimizing the scheduling of energy storage resources, extending the service life of equipment, and ensuring the stability of power supply. In summary, based on the characteristic parameters of charge and discharge capacity loss, the process of weighted statistics of the number of cycles of the target energy storage system to obtain the capacity decay coefficient not only demonstrates the complete chain from time series analysis to capacity decay coefficient calculation, but also reflects the advanced concepts and technical means in modern energy storage management systems. In this process, each technical link is closely linked, forming a powerful evaluation framework, which enables us to more accurately grasp the actual status of the energy storage system and make reasonable scheduling decisions in time to maintain the stability of the grid frequency and the quality of power. The application of this method is of great significance for optimizing the efficiency of energy storage resources and improving the flexibility and reliability of the power system.

[0038] In a specific embodiment, formulating a dispatching control plan based on the available capacity and the power grid operation trend includes: The available capacity and the grid operation trend are modeled in a state association manner through a dynamic programming algorithm to obtain available capacity-grid operation trend association data; wherein the available capacity-grid operation trend association data includes energy storage capacity state and load power deviation of the target grid; A preset fuzzy neural network is used to quantitatively evaluate the available capacity-grid operation trend correlation data to obtain a state evaluation result; wherein the state evaluation result includes a capacity margin parameter and a grid stability parameter; The state assessment result is monitored, and if any of the capacity margin parameter, the grid stability parameter, and the load balance parameter in the state assessment result is not within a preset range, a multi-objective weight allocation is performed on the state assessment result by using a hybrid particle swarm optimization algorithm to obtain a scheduling priority sequence; Performing scheduling instruction conversion on the scheduling priority sequence to obtain a preliminary scheduling instruction sequence; The preliminary scheduling instruction sequence is time-coordinated through a distributed collaborative optimization algorithm to obtain a scheduling control plan.

[0039] Specifically, the process of formulating a dispatching control plan based on the available capacity and the grid operation trend is a crucial step in the energy storage network control method. This process not only involves an in-depth analysis of the available capacity of the energy storage system and the grid operation trend, but also combines a variety of advanced technologies and methods such as dynamic programming algorithms, fuzzy neural networks, hybrid particle swarm optimization algorithms, and distributed collaborative optimization algorithms to ensure that the final dispatching control plan can effectively maintain the target grid frequency stability. The following is a specific explanation of this complex scheme. First, the available capacity and the grid operation trend are state-associated modeled by the dynamic programming algorithm to obtain available capacity-grid operation trend association data. The dynamic programming algorithm here is a mathematical optimization method for solving multi-stage decision problems. It can decompose complex system behaviors into a series of simple sub-problems and gradually solve the optimal solution. In this process, the dynamic programming algorithm comprehensively considers the current available capacity of the energy storage system and the prediction of future grid operation trends to construct a model that reflects the relationship between the two. For example, in an urban power grid containing multiple wind farms, if it is predicted that the wind speed will drop in the next few hours, resulting in a decrease in wind power output, the dynamic programming algorithm can help us understand which energy storage devices can be put into use when in this case to make up for the possible energy gap. The result of this association modeling forms the so-called available capacity-grid operation trend association data, including the energy storage capacity status and the load power deviation of the target grid, which is crucial for the subsequent quantitative evaluation. Next, the preset fuzzy neural network is used to quantitatively evaluate the available capacity-grid operation trend association data to obtain the status evaluation result. Fuzzy neural network is an artificial intelligence technology that combines the advantages of fuzzy logic and neural network. It can handle uncertainty and nonlinear problems and is very suitable for the complex environment of power systems. By quantitatively evaluating the association data, the fuzzy neural network can calculate two key parameters: capacity margin parameter and grid stability parameter. The capacity margin parameter reflects whether the energy storage system has sufficient remaining capacity to cope with the upcoming demand changes; while the grid stability parameter measures the degree to which the grid maintains the stability of frequency and other performance indicators under current conditions. For example, in the aforementioned urban power grid application scenario, when a certain energy storage unit is close to a fully charged state, its capacity margin parameter may be low, indicating that the unit will be difficult to provide additional support in the future; on the contrary, if the frequency fluctuation of the grid is small and the voltage level is stable, the grid stability parameter is high, indicating that the grid is currently in a good operating state. Furthermore, the state assessment results are monitored. If any capacity margin parameter, grid stability parameter or load balance parameter in the state assessment results is not within a preset range, a multi-objective weight allocation is performed on the state assessment results through a hybrid particle swarm optimization algorithm to obtain a scheduling priority sequence.The monitoring mentioned here refers to the continuous tracking of changes in the state assessment results to ensure that they always meet the expected standards. Once it is found that some parameters deviate from the normal range, immediate measures need to be taken to adjust them. The hybrid particle swarm optimization algorithm is a heuristic search algorithm that can find the global optimal solution in a multidimensional space and is suitable for dealing with complex optimization problems involving multiple objectives. In this case, the hybrid particle swarm optimization algorithm can determine how to prioritize the use of various energy storage units and other adjustment resources according to different weight allocation strategies, thereby generating a reasonable scheduling priority sequence. For example, in practical applications, if the load demand suddenly increases within a certain period of time, resulting in a decrease in the grid stability parameters, the stability of the grid can be quickly restored by adjusting the charging and discharging sequence and intensity of the energy storage unit. On this basis, the scheduling priority sequence is converted into a scheduling instruction to obtain a preliminary scheduling instruction sequence. Scheduling instruction conversion refers to converting an abstract priority sequence into a specific operation command, such as the charging and discharging power setting value, input or removal time of the energy storage unit, etc. This step ensures that the scheduling plan can be executed and also provides a basis for subsequent timing coordination. For example, in the above-mentioned example of the urban power grid, according to the dispatch priority sequence, it can be decided to first enable those energy storage units with good health and sufficient capacity to respond to peak loads, while arranging other units to be recharged during off-peak periods. Such dispatch instructions not only help to improve energy utilization efficiency, but also enhance the overall flexibility of the power grid. Finally, the preliminary dispatch instruction sequence is time-coordinated by a distributed collaborative optimization algorithm to obtain a dispatch control plan. The distributed collaborative optimization algorithm is a technology that can achieve synchronous optimization of multi-agent systems in a distributed environment. It is particularly suitable for handling complex dispatch tasks across regions and departments. In this way, it can ensure that the operations between different energy storage units are coordinated with each other to avoid the occurrence of overall suboptimal situations caused by local optimality. For example, in a large urban power grid, multiple energy storage units are distributed in different substations, and each unit has formulated preliminary dispatch instructions based on its own state evaluation results. However, due to the problem of resource competition or uneven load distribution between the units, it is necessary to readjust the timing of these instructions through a distributed collaborative optimization algorithm to make the dispatch of the entire power grid more efficient and harmonious. The final dispatch control plan can not only meet the immediate power demand, but also maximize economic and social benefits. In summary, the process of formulating a dispatch control plan based on the available capacity and the grid operation trend not only demonstrates the complete chain from state association modeling to timing coordination, but also reflects the advanced concepts and technical means in modern energy storage management systems. In this process, each technical link is closely linked to form a powerful dispatch framework, which allows us to more accurately grasp the actual state of the energy storage system and make reasonable dispatch decisions in a timely manner to maintain grid frequency stability and good power quality.The application of this method is of great significance for optimizing the utilization efficiency of energy storage resources and improving the flexibility and reliability of power systems.

[0040] The energy storage network control method in the embodiment of the present invention is described above. The energy storage network control system in the embodiment of the present invention is described below. Figure 2 In one embodiment of the present invention, an energy storage network control system includes: The acquisition module 21 is used to collect real-time data of the target power grid through intelligent sensors to obtain power grid acquisition data; A prediction module 22, used to predict the power grid collected data by using a preset big data analysis technology to obtain a power grid operation trend; An evaluation module 23 is used to evaluate the energy state of the target energy storage system to obtain the available capacity of the target energy storage system; The control module 24 formulates a dispatching control plan based on the available capacity and the grid operation trend; wherein the dispatching control plan is used to dispatch and control the target grid or target energy storage system to keep the target grid frequency stable.

[0041] In this embodiment, for the specific implementation of each unit in the above system embodiment, please refer to the above method embodiment, which will not be repeated here.

[0042] Reference Figure 3 The present invention also provides a computer device in an embodiment, wherein the internal structure of the computer device can be as follows: Figure 3 As shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0043] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0044] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented. It can be understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0045] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided by the present invention and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM.

[0046] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0047] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for controlling energy storage network, characterized in that: The following steps are involved: Real-time data collection of the target power grid is performed through intelligent sensors to obtain power grid collection data; The power grid collected data is predicted by using a preset big data analysis technology to obtain a power grid operation trend; Evaluate the energy status of the target energy storage system to obtain the available capacity of the target energy storage system; A dispatching control plan is formulated based on the available capacity and the operating trend of the power grid; wherein the dispatching control plan is used to dispatch and control the target power grid or target energy storage system to keep the frequency of the target power grid stable.

2. The energy storage network control method according to claim 1, characterized in that: The smart sensor is a distributed optical fiber sensor array. The smart sensor is used to collect real-time data of the target power grid to obtain power grid collection data, including: Sampling key nodes of the target power grid through the distributed optical fiber sensor array to obtain original electrical parameter data; Performing wavelet packet transform analysis on the original electrical parameter data to obtain time-frequency electrical characteristic data; Using a preset tensor decomposition algorithm, a high-order singular value decomposition is performed on the time-frequency electrical characteristic data to obtain a key characteristic tensor; wherein the key characteristic tensor includes the time-frequency characteristics of power consumption fluctuation, the time-frequency distribution of power load and the time-frequency characteristics of power spectrum; The key feature tensor is subjected to dimensionality reduction processing to obtain power grid acquisition data.

3. The energy storage network control method according to claim 1, characterized in that: The predicting of the power grid collected data by using a preset big data analysis technology to obtain the power grid operation trend includes: Performing data preprocessing on the power grid collected data to obtain preprocessed electrical data; Performing feature extraction on the preprocessed electrical data to obtain electrical extraction features; wherein the electrical extraction features; Performing time series data decomposition on the electrical extraction features to obtain multi-scale time series electrical features; wherein the multi-scale time series electrical features include load change features, voltage time domain features, current time domain features, power quality features, and power factor dynamic characteristics; Performing causal analysis on the electrical characteristics of the multi-scale time series by using a migration entropy algorithm to obtain a feature association network; wherein the feature association network is used to reveal the dynamic operation association between the electrical characteristics of each multi-scale time series in the target power grid; Using an adaptive kernel density estimation method, the state transition probability of the target power grid is estimated based on the feature association network to obtain a power grid state transition probability table; wherein the power grid state transition probability table is used to quantify the conversion probability of the target power grid between different working conditions, thereby performing a quantitative dynamic probability prediction of the future power grid operation state; Performing Markov chain prediction on the target power grid based on the power grid state transition probability table to obtain a power grid state prediction sequence; A self-organized criticality analysis method is used to perform nonlinear dynamic analysis on the grid state prediction sequence to obtain a grid operation trend; wherein the grid operation trend includes voltage stability, frequency stability, load change trend, and power quality change trend.

4. The energy storage network control method according to claim 1, characterized in that: The energy state evaluation of the target energy storage system to obtain the available capacity of the target energy storage system includes: Real-time current data can be obtained by monitoring the target energy storage system through intelligent sensors; Using a segmented coulomb counter method, the accumulated charge of the target energy storage system is calculated based on the real-time current data to obtain an accumulated charge sequence; Performing multi-scale decomposition on the accumulated charge sequence by a multi-resolution analysis method to obtain a multi-scale accumulated charge feature; Dynamically adjusting the multi-scale cumulative charge characteristics using an adaptive filtering algorithm to obtain a cumulative charge estimation value; Performing storage voltage and storage charge relationship analysis on the target energy storage system based on the estimated value of the accumulated charge amount to obtain a storage voltage-storage charge characteristic curve; The rated capacity of the target energy storage system and the performance record of the target energy storage system are obtained from the database, and the available capacity of the target energy storage system is dynamically evaluated and reversely calculated through the storage voltage-storage charge characteristic curve, the rated capacity and the performance record to obtain the available capacity of the target energy storage system.

5. The energy storage network control method according to claim 4, characterized in that: Based on the dynamic evaluation and reverse calculation of the available capacity of the target energy storage system through the storage voltage-storage charge characteristic curve, the rated capacity and the performance record, the available capacity of the target energy storage system is obtained, including: Using a piecewise exponential fitting algorithm to perform piecewise fitting processing on the storage voltage-storage charge characteristic curve to obtain a charge and discharge capacity loss characteristic parameter; Based on the charge and discharge capacity loss characteristic parameter, weighted statistics of the number of cycles of the target energy storage system are performed to obtain a capacity attenuation coefficient; Performing collaborative analysis on the target energy storage system based on the capacity attenuation coefficient and the performance record to obtain a health status of the target energy storage system; Based on the health status and the rated capacity, a dynamic capacity evaluation is performed on the current energy storage system to obtain a theoretical available capacity value; Acquiring a current temperature parameter through a preset temperature sensor, and performing capacity value compensation on the theoretical available capacity value based on the current temperature parameter to obtain a temperature corrected capacity value; Based on the temperature-corrected capacity value, the charge-discharge capacity loss characteristic parameter, the capacity attenuation coefficient and the health status, the available capacity of the target energy storage system is reversely calculated to obtain the available capacity of the target energy storage system.

6. The energy storage network control method according to claim 5, characterized in that: Based on the charge and discharge capacity loss characteristic parameter, weighted statistics of the number of cycles of the target energy storage system are performed to obtain a capacity attenuation coefficient, including: Performing time series analysis on the charge and discharge capacity loss characteristic parameters to obtain a charge and discharge cycle sequence; Based on the charge and discharge cycle sequence, counting the number of charge and discharge times of each cycle of the target energy storage system to obtain a cycle number data set; Performing weighted average calculation on the cycle number data set using a weighted moving average method to obtain a weighted cycle number; Based on the weighted number of cycles, a capacity loss of the energy storage system is fitted and analyzed to obtain a capacity decay curve; Performing slope analysis on the capacity decay curve to obtain a capacity decay rate; Based on the capacity decay rate and the weighted number of cycles, a capacity decay coefficient is calculated for the target energy storage system to obtain a capacity decay coefficient.

7. The energy storage network control method according to claim 1, characterized in that: The formulating of a dispatching control plan based on the available capacity and the power grid operation trend includes: The available capacity and the grid operation trend are modeled in a state association manner through a dynamic programming algorithm to obtain available capacity-grid operation trend association data; wherein the available capacity-grid operation trend association data includes energy storage capacity state and load power deviation of the target grid; A preset fuzzy neural network is used to quantitatively evaluate the available capacity-grid operation trend correlation data to obtain a state evaluation result; wherein the state evaluation result includes a capacity margin parameter and a grid stability parameter; The state assessment result is monitored, and if any of the capacity margin parameter, the grid stability parameter, and the load balance parameter in the state assessment result is not within a preset range, a multi-objective weight allocation is performed on the state assessment result by using a hybrid particle swarm optimization algorithm to obtain a scheduling priority sequence; Performing scheduling instruction conversion on the scheduling priority sequence to obtain a preliminary scheduling instruction sequence; The preliminary scheduling instruction sequence is time-coordinated through a distributed collaborative optimization algorithm to obtain a scheduling control plan.

8. An energy storage network control device, characterized in that: include: The acquisition module is used to collect real-time data of the target power grid through intelligent sensors to obtain power grid acquisition data; A prediction module, used to predict the collected data of the power grid by using a preset big data analysis technology to obtain the operation trend of the power grid; An evaluation module is used to evaluate the energy state of the target energy storage system and obtain the available capacity of the target energy storage system; A control module formulates a dispatching control plan based on the available capacity and the power grid operation trend; wherein the dispatching control plan is used to dispatch and control the target power grid or target energy storage system to keep the target power grid frequency stable.

9. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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