Rural digital twin energy storage management method and system based on Internet of Things

Abstract

The invention relates to the technical field of digital twinning, in particular to a rural digital twinning energy storage management method and system based on the Internet of Things. The method comprises the following steps: collecting real-time operation data through rural distributed energy storage equipment to obtain rural energy storage data; constructing a digital twinborn model according to the rural energy storage data to obtain a rural energy storage digital twinborn model; performing energy storage state detection according to the rural energy storage digital twinborn model to obtain rural energy storage state data; and performing optimization scheduling according to the rural energy storage state data to obtain rural energy storage scheduling data so as to perform rural energy storage management auxiliary operation. Through construction of the digital twinborn model and virtual-real synchronous management, the system can realize multi-level state monitoring, load prediction and priority scheduling of the equipment, and ensures efficient operation of the energy storage equipment in a rural power grid environment.

Description

Technical Field

[0001] The present invention relates to the field of digital twin technology, and in particular to a rural digital twin energy storage management method and system based on the Internet of Things. Background Art

[0002] With the rapid development of renewable energy and distributed energy systems, especially in rural areas, more and more rural distributed energy storage systems are being used in agricultural production, rural power grid peak regulation and clean energy power supply. However, due to the complex environment, large fluctuations in load demand and a wide variety of equipment in actual application of energy storage systems, traditional manual management and timed monitoring methods are difficult to effectively cope with the dynamic load demand and complex operation of energy storage equipment. Traditional management methods are not only inefficient and slow to respond, but also have a significant impact on the life of energy storage equipment and system energy efficiency. Summary of the invention

[0003] In order to solve the above technical problems, the present invention proposes a rural digital twin energy storage management method and system based on the Internet of Things to solve at least one of the above technical problems.

[0004] The present application provides a rural digital twin energy storage management method based on the Internet of Things, the method comprising:

[0005] S1. Collect real-time operation data through rural distributed energy storage equipment to obtain rural energy storage data;

[0006] S2. Construct a digital twin model based on rural energy storage data to obtain a rural energy storage digital twin model;

[0007] S3. Perform energy storage status detection based on the rural energy storage digital twin model to obtain rural energy storage status data;

[0008] S4. Optimize the scheduling based on the rural energy storage status data to obtain rural energy storage scheduling data to assist in rural energy storage management operations.

[0009] Based on real-time data, the present invention can quickly respond to changes in energy storage equipment and ensure stable operation of the system. The digital twin model can perform virtual simulation of the physical system and provide accurate performance analysis, so that the system can make accurate analysis. By simulating different operating scenarios, the energy storage management strategy is optimized and the overall efficiency of the system is improved. By performing status detection based on the digital twin model, potential problems (such as equipment failure or performance degradation) can be discovered in a timely manner to reduce maintenance costs. By optimizing the scheduling algorithm, the optimal operation of energy storage equipment under different load conditions is ensured and energy utilization is improved. Through efficient energy storage management, the integration of renewable energy is supported and sustainable development in rural areas is promoted.

[0010] Optionally, S1 includes:

[0011] S11. Collect real-time operation data through rural distributed energy storage equipment to obtain real-time energy storage operation data;

[0012] S12, performing stratified processing according to the real-time operation data of the energy storage to obtain energy storage stratified data;

[0013] S13. Synchronize data according to the energy storage stratification data to obtain rural energy storage data.

[0014] In the present invention, real-time operation data is collected through rural distributed energy storage equipment, and the system can obtain the most accurate and latest operation information, which is conducive to rapid response and decision-making. Through hierarchical processing, the original data is classified as needed, redundant information can be filtered out, the amount of data can be reduced, and the system processing efficiency can be improved. Hierarchical processing divides the data into different characteristic categories (such as temperature, load, voltage, etc.), which is convenient for model construction and analysis and enhances the operability of the data. The results of hierarchical processing can accurately reflect the multi-dimensional state of the energy storage system and provide a clear basis for subsequent precise optimization. Through synchronous processing, hierarchical data from different sources and at different times are integrated to form complete rural energy storage data, which is convenient for global optimization management. During the data synchronization process, ensure the seamless transmission of data from each device to the central system to avoid the loss of important information and ensure the integrity of the data.

[0015] Optionally, the layered processing includes:

[0016] Performing device type stratification processing according to the real-time energy storage operation data to obtain first energy storage stratification data;

[0017] Performing electrical characteristic stratification processing according to the real-time energy storage operation data to obtain second energy storage stratification data;

[0018] Performing time dimension stratification processing according to the real-time operation data of energy storage to obtain third energy storage stratification data;

[0019] A hierarchical network is constructed according to the first energy storage hierarchical data, the second energy storage hierarchical data and the third energy storage hierarchical data to obtain energy storage hierarchical data.

[0020] In the present invention, stratification by device type can manage different types of energy storage devices (such as battery type, energy storage system type) separately, which helps to optimize the operation strategy of each type of equipment in a targeted manner. Different equipment types have different working characteristics. After the equipment type is stratified, the same type of equipment can be compared and analyzed, which is convenient for rapid diagnosis of abnormal equipment. Stratification by electrical characteristics (such as voltage, current, power, temperature) can help to more intuitively observe and analyze the changes in electrical parameters, especially when overloaded or abnormal. According to the stratification processing of electrical characteristics, the anomalies in different electrical characteristics can be quickly identified and located, thereby reducing the difficulty and time of fault detection. Stratification by time dimension (such as minutes, hours, days) helps to understand the changing trends of load and energy storage, and provides support for short-term load forecasting and long-term energy storage planning. Time stratification can identify the periodic changes and peak load periods of the energy storage system, facilitate scheduling optimization, and improve the balance and response speed of the energy storage system.

[0021] Optionally, the device type hierarchical processing includes:

[0022] Classify the equipment types according to the real-time operation data of energy storage to obtain the classification data of energy storage equipment;

[0023] Extract subsystem data according to energy storage device classification data to obtain energy storage subsystem classification data;

[0024] Classify the operation characteristics according to the classification data of the energy storage subsystem to obtain the energy storage operation characteristic data;

[0025] Device characteristics are aggregated according to the energy storage operation characteristic data to obtain first energy storage hierarchical data.

[0026] In the present invention, different energy storage devices (such as lithium batteries, lead-acid batteries, thermal energy storage devices, etc.) can be separated and managed by classifying them according to the device type, so as to achieve targeted maintenance and optimization. On the basis of equipment classification, subsystem data (such as battery management system, temperature control system, charging and discharging module, etc.) is further extracted to make the data more refined and improve the monitoring accuracy of the system. Classifying subsystem data according to different operating characteristics (such as temperature, voltage, current, etc.) helps to clearly display the real-time status of energy storage equipment. Aggregating multi-level characteristic data into device characteristics can significantly reduce data redundancy and facilitate management and storage.

[0027] Optionally, the electrical characteristics layering process includes:

[0028] Electrical parameters are identified according to real-time operation data of energy storage to obtain classification data of electrical parameters of energy storage;

[0029] Process the voltage and current characteristics according to the energy storage electrical parameter classification data to obtain the energy storage voltage and current distribution data;

[0030] Perform load balancing according to real-time operation data of energy storage to obtain energy storage load characteristic data;

[0031] Perform power characteristic analysis based on energy storage electrical parameter classification data to obtain energy storage power characteristic data;

[0032] Perform temperature efficiency correlation analysis based on energy storage real-time operation data, energy storage voltage and current distribution data, and energy storage power characteristic data to obtain energy storage temperature efficiency data;

[0033] Electrical characteristics aggregation is performed according to the energy storage electrical parameter classification data, the energy storage voltage and current distribution data, the energy storage load characteristic data, the energy storage power characteristic data, and the energy storage temperature efficiency data to obtain second energy storage hierarchical data;

[0034] The load balancing features include:

[0035] Extract load characteristics based on real-time operation data of energy storage to obtain energy storage load characteristic data;

[0036] Perform load clustering processing according to energy storage load characteristic data to obtain energy storage load clustering data;

[0037] Performing graph load characteristic processing according to energy storage load clustering data to obtain energy storage load graph data;

[0038] Calculate the mixed load balance index according to the energy storage load diagram data to obtain the energy storage load mixed index data;

[0039] The energy storage load characteristic data is obtained by performing time series sensitivity processing according to the energy storage load mixed index data.

[0040] In the present invention, electrical parameters (such as voltage, current, power, etc.) are identified to classify real-time operating data according to different electrical characteristics, laying a structured foundation for data analysis and processing. Distribution analysis of voltage and current data helps to understand the voltage and current conditions of each node and discover problems such as overvoltage, undervoltage or abnormal current. By analyzing the load balancing characteristics of the energy storage system, the operating status of the system under different load conditions can be identified to facilitate load allocation and management. Through power characteristic analysis, the power efficiency of the energy storage system can be evaluated and links with high energy consumption can be identified in order to formulate energy-saving measures. Through temperature-efficiency correlation analysis, the impact of temperature changes on equipment efficiency can be evaluated, the temperature control strategy can be optimized, and the equipment life can be extended. Electrical characteristic aggregation data helps to optimize the overall scheduling strategy of the energy storage system and ensure the balanced operation of the system under different electrical characteristic dimensions.

[0041] In the present invention, by extracting load characteristics from real-time operation data of energy storage, key load characteristics (such as load fluctuations, load peaks, etc.) in the energy storage system can be captured. The clustered load data presents the load status of the energy storage system in a hierarchical manner, simplifies the process of formulating scheduling strategies, and improves the accuracy of scheduling. Through graph load characteristic processing, the correlation between load data is established using the graph structure, and the interdependence and conduction effects of load nodes are mined. The hybrid index calculation can combine the current load status and historical data to dynamically adjust the evaluation criteria for load balance, so that the system can adapt to different load changes. Timing sensitivity analysis can identify the sensitivity of the load balance index to time, predict load change trends, and improve the predictability of the system.

[0042] Optionally, the time dimension hierarchical processing includes:

[0043] Perform time labeling processing based on the real-time operation data of energy storage to obtain energy storage time label data;

[0044] Perform a first time characteristic analysis based on the energy storage time tag data to obtain short-term time characteristic data;

[0045] Perform a second time characteristic analysis based on the energy storage time tag data to obtain mid-term time characteristic data;

[0046] Perform a third time characteristic analysis based on the energy storage time tag data to obtain long-term time characteristic data, wherein the time data corresponding to the first time characteristic analysis is less than or equal to the time data corresponding to the second time characteristic analysis, and the time data corresponding to the second time characteristic analysis is less than or equal to the time data corresponding to the third time characteristic analysis;

[0047] According to the short-term time characteristic data, the medium-term time characteristic data and the long-term time characteristic data, the peak and valley time periods are segmented to obtain the peak and valley characteristic data;

[0048] The time correlation characteristics of the short-term time characteristic data, the medium-term time characteristic data and the long-term time characteristic data are extracted according to the peak-valley characteristic data to obtain the third energy storage stratification data.

[0049] The time-labeled data in the present invention can be hierarchically analyzed at three scales: short-term, medium-term, and long-term, so as to facilitate the identification of characteristics at different time scales. Through the analysis of short-term, medium-term, and long-term time characteristics, the load and energy storage characteristics of the energy storage system at different time scales can be captured, providing a basis for optimal scheduling at multiple time scales. By identifying the peak and valley time periods, charging can be performed when the load is low and discharging can be performed when the load is peak, so as to achieve load balancing and energy efficiency optimization of the energy storage equipment. Peak and valley time segmentation can help the energy storage system charge during the valley period of low electricity prices and discharge during the peak period, thereby saving electricity costs and maximizing the economic benefits of the energy storage system. Through the extraction of time-related characteristics, the short-term, medium-term, and long-term time characteristics are correlated and analyzed, which helps to identify the connection between characteristics at different time scales. Time-related characteristic data can reveal the correlation between different time characteristics and provide more accurate data support for load forecasting and scheduling optimization.

[0050] Optionally, S2 includes:

[0051] S21. Perform digital twin mapping and construction based on rural energy storage data to obtain an initial digital twin model of rural energy storage;

[0052] S22. Extract energy storage system features based on the initial digital twin model of rural energy storage to obtain energy storage system feature data;

[0053] S23, constructing a real-time response layer according to the characteristic data of the energy storage system to obtain real-time response layer data of the energy storage system;

[0054] S24, pairing virtual and real characteristics according to the initial digital twin model of rural energy storage to obtain virtual and real characteristic calibration table data;

[0055] S25. The initial rural energy storage digital twin model is verified according to the real-time response layer data of the energy storage system and the virtual-real characteristic calibration table data to obtain the rural energy storage digital twin model.

[0056] In the present invention, a virtual mapping of the energy storage system is created by constructing a digital twin mapping model, which provides a basis for subsequent performance analysis, fault detection and optimized scheduling. The multi-dimensional feature data (such as temperature, current, voltage, load status, etc.) of the energy storage system are extracted from the initial digital twin model to facilitate the analysis of the key characteristics of the system. By constructing a real-time response layer, the energy storage system can respond quickly when changes or anomalies occur, thereby improving the dynamic adaptability of the system. By pairing virtual and physical characteristics, accurate calibration between the virtual model and the physical device is achieved to ensure that the digital twin model can truly reflect the status of the physical device. By verifying the initial digital twin model, the accuracy and reliability of the digital twin model are ensured to prevent deviations in actual applications.

[0057] Optionally, S3 includes:

[0058] S31. Extract energy storage status according to the rural energy storage digital twin model to obtain energy storage status data;

[0059] S32, performing abnormal state detection according to the energy storage state data to obtain energy storage abnormal state data;

[0060] S33, performing energy storage health assessment according to the energy storage state data and the energy storage abnormal state data to obtain energy storage health data;

[0061] S34, obtaining historical energy storage usage data, and estimating the energy storage life according to the historical energy storage usage data and energy storage health data to obtain energy storage life estimation data;

[0062] S35, calculating energy utilization efficiency according to the energy storage state data to obtain energy storage energy utilization efficiency data;

[0063] S36. Perform real-time risk processing on energy storage life estimation data and energy storage energy efficiency data to obtain rural energy storage status data.

[0064] In the present invention, the real-time status data of the energy storage system, including current, voltage, temperature, load, etc., is extracted through a digital twin model, so that the operating status of the equipment can be mastered at any time. Through real-time abnormal status detection, the system can quickly identify faults or abnormal conditions in the energy storage equipment, such as excessive temperature, abnormal current, etc., so as to take timely countermeasures. By using status data and abnormal data for health assessment, the health status of the energy storage equipment can be accurately assessed, including battery life, temperature control effect, etc. The energy storage life estimation based on health data and historical usage data helps predict the remaining life of the equipment, thereby optimizing the equipment usage plan. Calculating the energy utilization efficiency of the energy storage system can identify links with low energy efficiency, thereby providing guidance for energy efficiency optimization. According to the energy storage life and energy efficiency data, the risk factors in the energy storage system can be identified in real time, which helps to quickly take early warning measures and avoid the expansion of risks.

[0065] Optionally, S4 includes:

[0066] S41. Perform load forecasting and demand matching according to rural energy storage status data to obtain load demand matching table data;

[0067] S42, setting a priority strategy according to the load demand matching table data to obtain energy storage scheduling priority plan data;

[0068] S43, performing dynamic charging and discharging window optimization according to the energy storage scheduling priority plan data to obtain dynamic charging and discharging schedule data;

[0069] S44. Coordinated dispatch of multiple devices is performed based on dynamic charge and discharge schedule data to obtain rural energy storage dispatch data for auxiliary operations in rural energy storage management.

[0070] In the present invention, by matching load forecast with demand, charging and discharging preparations can be made in advance during peak load periods, and charging can be reasonably arranged during off-peak periods to ensure that energy supply meets demand. A priority strategy is set so that key loads (such as agricultural irrigation, important power supply loads, etc.) are given priority in power support when energy is scarce, ensuring the stable operation of the basic functions of the system. Dynamic charging and discharging window optimization selects the best charging and discharging time according to load demand and electricity price conditions, ensuring charging at low electricity prices and discharging at peak electricity prices, thereby improving the economic benefits of the system. Collaborative scheduling of multiple devices can evenly distribute the load, ensure that each energy storage device operates within a reasonable load range, avoid overloading of a single device, and improve equipment stability. Collaborative scheduling enables the collaborative work of multiple energy storage devices, and can respond quickly to high loads or sudden demands, thereby improving the scheduling efficiency and power supply reliability of the system.

[0071] Optionally, the present application also provides a rural digital twin energy storage management system based on the Internet of Things, which is used to execute the rural digital twin energy storage management method based on the Internet of Things as described above, and the rural digital twin energy storage management system based on the Internet of Things includes:

[0072] Rural energy storage real-time collection module, used to collect real-time operation data through rural distributed energy storage equipment to obtain rural energy storage data;

[0073] A digital twin model construction module is used to construct a digital twin model based on rural energy storage data to obtain a rural energy storage digital twin model;

[0074] The energy storage status detection module is used to detect the energy storage status according to the rural energy storage digital twin model to obtain rural energy storage status data;

[0075] The rural energy storage dispatching module is used to optimize dispatching according to rural energy storage status data and obtain rural energy storage dispatching data to assist in rural energy storage management operations.

[0076] The objects of the present invention are:

[0077] 1. Collecting real-time operation data through rural distributed energy storage equipment provides real-time and accurate information support for modeling, monitoring and optimized scheduling, significantly improving the system's response speed to equipment status and environmental changes. Adopting hierarchical processing and synchronization mechanisms to ensure structured, streamlined storage and consistency across devices not only saves data processing time, but also improves data reliability, allowing the system to more efficiently grasp the operating status and load requirements of energy storage equipment from a global level;

[0078] 2. The digital twin model establishes an accurate virtual mapping for the physical energy storage system. Through intelligent analysis and feature extraction of energy storage data, it synchronizes the various characteristics and operating status of the equipment into the virtual environment, providing a global visual view of the system. The model not only simplifies the monitoring of the energy storage system, but also improves the transparency of the system, allowing users to observe and control the performance of energy storage equipment in real time. By building a "real-time response layer" and "virtual-real characteristic pairing", the digital twin model can maintain high-precision synchronization with physical equipment. Virtual-real pairing achieves precise calibration of the system, ensuring that the digital twin model can truly reflect the status of the equipment at any time, and providing highly reliable data support for optimized scheduling and fault detection. By performing energy storage status detection on the digital twin model, multi-dimensional monitoring of the equipment (such as temperature, current, voltage, load, etc.) is achieved;

[0079] 3. Abnormal state detection can timely identify potential operating risks, prevent the risk of system downtime caused by sudden failures, and greatly improve the safety of the equipment. Based on load forecasting and priority strategies, the charging and discharging windows are dynamically adjusted. The system charges during off-peak hours and discharges during peak hours, which improves the utilization efficiency of energy storage resources and reduces energy storage costs. In addition, multi-device coordinated scheduling can balance load pressure during peak load hours, ensuring that multiple energy storage devices operate efficiently within a reasonable load range, thereby improving the overall stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting implementations made with reference to the following drawings:

[0081] Figure 1 A flowchart of a rural digital twin energy storage management method based on the Internet of Things is shown in one embodiment;

[0082] Figure 2 A flowchart showing a method for real-time collection of rural energy storage according to an embodiment of the present invention is provided;

[0083] Figure 3 A flowchart showing a method for constructing a digital twin model according to an embodiment of the present invention is shown;

[0084] Figure 4 A flow chart showing the steps of a method for detecting energy storage status according to an embodiment is shown;

[0085] Figure 5 A flowchart showing a method for dispatching rural energy storage according to an embodiment of the present invention is provided;

[0086] 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

[0087] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0088] In addition, the drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0089] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0090] A distributed energy storage system was installed in a rural area, which includes 5 energy storage units (batteries), each with a rated capacity of 50kWh. The energy storage devices in the system support real-time collection of voltage, current, temperature and other data, and transmit them to the cloud platform through IoT devices for use in the digital twin model. Real-time data of each energy storage unit is collected through IoT devices, including: voltage: 240V, current: 120A, temperature: 25℃, charge and discharge status: charging, current storage capacity: 40kWh (80% remaining) After hierarchical processing and synchronous transmission, the cloud obtains the rural energy storage data set.

[0091] Based on the collected energy storage data, the system builds a digital twin model to enable virtual-real mapping and real-time response of energy storage status. A digital twin object is created for each energy storage unit in the cloud, and the initial parameters are set to: 240V, 120A, 25℃, charging status. The digital twin model receives real-time data from each device at regular intervals and updates the operating status of the virtual device. The system aggregates and analyzes characteristic data such as voltage, current, and temperature. For example, in the past hour, the temperature has been stable in the range of 25-28℃, and the voltage has been maintained at 235-245V. The real-time response layer of the model is built to enable the system to dynamically adjust according to real-time data. For example, if the current exceeds 130A, an alarm is triggered and the charging rate is reduced. Feature pairing is performed between each physical device and its digital twin object to ensure that the model accurately reflects the status of the device. If the voltage is lower than 230V or the temperature exceeds 30℃, an early warning is triggered. Through data feedback from the real-time response layer, the status of the virtual model and the physical equipment are compared to ensure that the data synchronization rate between the two is above 95%. The verified model is used as a rural energy storage digital twin model for real-time monitoring, charging and discharging optimization, and risk prediction.

[0092] The real-time response layer is the core module of the digital twin model, which is used to receive, process and feedback the real-time data of the energy storage equipment. The core architecture of this layer can be divided into four main modules: data acquisition and synchronization module, state monitoring and anomaly detection module, dynamic decision and control module, feedback and adaptive optimization module.

[0093] The data acquisition and synchronization module collects real-time data streams from various energy storage devices (such as battery cells, temperature control systems, charging and discharging modules, etc.) to ensure that the system receives multi-dimensional data inputs, such as voltage, current, temperature, load status, etc.

[0094] The status monitoring and anomaly detection module monitors the key status parameters of the energy storage system in real time by analyzing data such as voltage, current, and temperature. Built-in machine learning models (such as time series analysis, anomaly detection algorithms) or rule-based detection mechanisms are used to identify abnormal device behavior (such as voltage exceeding the limit, abnormal current fluctuations, and excessive temperature). When an abnormal state is detected, the system will immediately trigger an early warning signal and mark and record relevant information about the abnormal device.

[0095] Dynamic decision-making and control module, real-time response layer built-in charging and discharging strategy, can dynamically adjust the charging and discharging behavior of energy storage equipment according to load demand and current electricity price (such as reducing excessive discharge current, delaying valley charging, etc.). Combined with the dispatch priority scheme, key equipment is dispatched first when power is scarce or load demand is high to ensure that the system can still operate stably under high load. According to the real-time monitored current, voltage and temperature data, the power output and temperature control strategy of the equipment are dynamically adjusted to ensure that the equipment operates in the best state.

[0096] The feedback and adaptive optimization module feeds back the adjustment data of the real-time response layer to the main database of the digital twin model so that parameters can be updated and adjusted in the virtual model. By analyzing and learning historical data, the response rules and control strategies of the real-time response layer are continuously adjusted to adapt to changes in load demand. The system can update the scheduling and control strategies based on the results of long-term monitoring, such as updating the charging and discharging windows, optimizing priority strategies, etc., to achieve higher energy efficiency.

[0097] The digital twin model is used to detect and analyze the energy storage status data, including anomaly detection, health assessment, and life estimation. Through feature analysis, the system detects an abnormal current situation: the current of energy storage unit 1 reaches 140A for a short time. The system immediately triggers an abnormal warning and records it in the energy storage abnormal status data. Based on the status data and abnormal data, the health of the energy storage unit is evaluated. After analysis, the health score of energy storage unit 1 is 80 / 100, and the scores of other energy storage units are between 85-90 / 100. Combining historical data and health data, the system predicts that the remaining life of energy storage unit 1 is 4 years, and the average remaining life of other energy storage units is 5 years. Generate energy storage life estimation data. The system calculates the energy efficiency of each energy storage unit and finds that the energy efficiency of energy storage unit 1 is 85% (slightly lower than 90% of other units), which is recorded in the energy storage energy efficiency data. Based on the life estimation and energy efficiency data, the energy storage unit 1 is risk-handled, and the system is advised to perform preventive maintenance on the unit and adjust its charging and discharging priority.

[0098] The system analyzes the load demand in the next 24 hours and predicts that the peak period is from 7 to 9 p.m., which requires 80kWh of energy storage support. The system prioritizes matching available energy storage devices and generates a load demand matching table. The system sets energy storage unit 1 to respond to demand at night, but at a lower charging rate to reduce load pressure. Other units are set to medium priority to dynamically respond to demand in other time periods and generate an energy storage scheduling priority plan. The energy storage scheduling priority plan is as follows:

[0099] Energy storage unit 1 is set to high priority to ensure that power is provided first during the high-load period at night (such as 19:00-23:00). Other energy storage units (units 2 to 5) are set to medium priority to dynamically respond to the needs of other periods to avoid multiple units discharging at the same time at night, which may cause excessive system load pressure. Energy storage unit 1 responds first during the high-load period at night, but sets a lower charging rate (for example, set to 20kW) to reduce the charging current and reduce the instantaneous load pressure on the power grid. Energy storage units 2 to 5 are charged first during the low-load period (such as 1:00-5:00 in the morning), and a higher charging rate (for example, set to 30-40kW) is set to ensure that they can be fully charged and on standby during the daytime. 1. Energy storage unit 1 (high priority), priority: high, response period: 19:00-23:00 (high-load demand period at night). Dispatching strategy: During the period from 19:00 to 23:00, energy storage unit 1 is discharged first to respond to the load demand at night. Set the charging rate to 20kW, and control the charging rate at night to avoid placing a high load on the power grid. Discharge target: Meet the peak demand for electricity at night and ensure that the main loads are supplied with stable power. 2. Energy storage units 2 to 5 (medium priority), priority: Medium response period: Dynamically respond to load demands in other periods (such as medium and low load demand periods during the day). Dispatching strategy: Prioritize charging during the low load period of 1:00-5:00 in the morning, and the charging rate is set to 30-40kW to ensure that they are fully charged during the daytime electricity demand. During non-peak hours during the day and night, be on standby at any time depending on the energy storage demand, and give priority to discharging during medium and low load demand periods (such as 10:00-16:00 during the day).

[0100] Fast charging during low-load periods ensures that auxiliary power support can be provided during peak hours during the day, reducing the load pressure on energy storage unit 1 during the day. According to the electricity price and load demand, the system sets the charging window in the off-peak period of 1-5 a.m. and the discharging window in the peak period of 7-9 p.m., optimizing the charging and discharging schedule to reduce energy consumption. According to the charging and discharging schedule, the system synchronously dispatches 5 energy storage units to discharge collaboratively during the peak period at night and charge during the off-peak period during the day. The coordinated scheduling scheme ensures that the 80kWh energy storage demand is met and the equipment load is effectively reduced, and finally obtains the rural energy storage scheduling data.

[0101] See also Figures 1 to 5 , the present application provides a rural digital twin energy storage management method based on the Internet of Things, the method comprising:

[0102] S1. Collect real-time operation data through rural distributed energy storage equipment to obtain rural energy storage data;

[0103] Specifically, the voltage, current, temperature, charge and discharge status and other parameters of distributed energy storage equipment are monitored in real time through sensors, smart meters, controllers and other equipment. The collected data is uploaded to the cloud data platform or local server through wireless communication modules (such as NB-IoT, LoRa, etc.) for subsequent analysis and model building.

[0104] S2. Construct a digital twin model based on rural energy storage data to obtain a rural energy storage digital twin model;

[0105] Specifically, based on the collected historical data and real-time data, the various parameters in the digital twin model are initialized to reflect the current state of the distributed energy storage system. The latest real-time data is continuously input into the model to synchronize the model with the state of the actual energy storage device. Based on the characteristics of the actual physical system and the collected data, a virtual scene consistent with the physical system is created in the digital twin model to simulate the operation. The model parameters are calibrated regularly to ensure the accuracy of the model, especially when the working conditions or environment of the equipment change.

[0106] S3. Perform energy storage status detection based on the rural energy storage digital twin model to obtain rural energy storage status data;

[0107] Specifically, the real-time data in the digital twin model is used to analyze the state changes of energy storage equipment, such as charging state, discharging state, battery life prediction, etc. By setting thresholds or rules, the abnormal state of energy storage equipment is automatically detected to determine whether there are risks such as abnormal temperature, charging overload or failure. The energy storage state information obtained by the detection is output and saved as the basis for further decision-making and scheduling.

[0108] S4. Optimize the scheduling based on the rural energy storage status data to obtain rural energy storage scheduling data to assist in rural energy storage management operations.

[0109] Specifically, based on historical energy storage status data and current demand, future energy storage demand is predicted based on deep learning algorithms such as regression models or vector machine algorithms to optimize the charging and discharging scheduling of the energy storage system. According to the demand forecast results and real-time status, the best scheduling strategy is formulated, such as giving priority to charging when the grid is under low load and giving priority to discharging when the grid is under high load, so as to achieve efficient utilization of the energy storage system. The scheduling data for controlling the energy storage equipment is generated, and the data includes parameters such as the charging and discharging power and time of each device. The scheduling execution effect is monitored in real time, and the scheduling parameters are adjusted in time according to the actual operation situation to ensure the continuous optimization of management auxiliary operations.

[0110] Specifically, based on time series prediction algorithms, such as regression functions or time-averaged shift algorithms, future demand is predicted based on historical data and digital twin models. For example, if the SOC decrease rate is 10% / day, and tomorrow's grid load is expected to be 80%, it is necessary to increase the charging plan in advance. Generate a scheduling strategy to improve energy storage utilization and stability. For example: Discharge is prioritized during peak power consumption during the day (such as 9 am to 6 pm). Charge is prioritized during low power consumption at night (such as 11 pm to 6 am). Scheduling data output: Convert scheduling strategies into data instructions. For example: Discharge instruction: 9 am to 6 pm, discharge 10 kWh per hour to ensure that daytime power demand is met. Charging instruction: 23 pm to 6 am, charge 15 kWh per hour to restore power reserves. Monitor the scheduling effect in real time. If the battery temperature rises too fast or the SOC changes abnormally, adjust the discharge or charging rate immediately. For example, reduce the discharge power when the temperature rises to 43°C to prevent overheating.

[0111] Optionally, S1 includes:

[0112] S11. Collect real-time operation data through rural distributed energy storage equipment to obtain real-time energy storage operation data;

[0113] Specifically, multiple sensors are installed on each energy storage device to collect different types of operating data, such as voltage, current, temperature, charge and discharge status, etc. At fixed time intervals (such as every 5 minutes), the sensor collects data to generate a set of real-time operating data samples, such as: voltage (V): 415V, current (A): 120A, temperature (℃): 32℃, SOC (State of Charge): 65%.

[0114] S12, performing stratified processing according to the real-time operation data of the energy storage to obtain energy storage stratified data;

[0115] Specifically, data is layered according to the data collection source, nature and time dimension. For example, the device layer data is independent real-time data for each device. The regional layer data is the comprehensive data of different devices, which is summarized as the average or total value of a certain area. The historical layer data is the statistical result of data over a certain period of time in the past (such as 1 hour) and is used for trend analysis. In the device layer, the data of each device is filtered and aggregated to calculate the state parameters of the current device. For example, the charging or discharging rate is calculated. In the regional layer, the data of multiple devices in the same area are integrated to obtain the average value of the voltage, current and SOC of the entire area. For example, the average SOC of 5 devices in the area is 68%. In the historical layer, based on the device and regional layers, long-term trend data (such as the average temperature in the past 24 hours) is calculated to facilitate subsequent optimization and scheduling. Refresh the layered data regularly (for example, update the device layer data every 5 minutes and update the regional layer data every 1 hour), and adjust the data in the layered structure according to the changes in real-time data to maintain the accuracy and timeliness of the data hierarchy.

[0116] S13. Synchronize data according to the energy storage stratification data to obtain rural energy storage data.

[0117] Specifically, select the type and scope of data to be synchronized, such as device status information, battery charge, temperature and other parameters. Selectively extract key indicators based on layered data to reduce the amount of transmitted data. For example, only extract SOC and temperature from the device layer, and extract average SOC and temperature from the regional layer. Ensure the consistency of data in each layer at the same time point. For example, align the device layer, regional layer, and historical layer data to the current timestamp to generate accurate rural energy storage data. Before synchronization, compress unimportant detail data and retain only core data to improve transmission efficiency. For example, summarize and compress the historical layer data into hourly averages instead of minute-by-minute data.

[0118] Optionally, the layered processing includes:

[0119] Performing device type stratification processing according to the real-time energy storage operation data to obtain first energy storage stratification data;

[0120] Specifically, the data is classified according to different types of equipment (such as battery type, installation location, capacity, etc.). For example: Battery type: lithium battery, lead-acid battery, etc. Installation location: indoors, outdoors. Capacity: small (≤100kWh), medium (100–500kWh), large (>500kWh). Generate independent real-time operating data for each type of equipment. For example, for lithium battery type equipment, generate its real-time SOC, temperature, voltage and other data. For each equipment category, calculate the average, total value or trend. For example: the average SOC of lithium battery type is 66%, and the average SOC of lead-acid battery is 62%. Combine the classified data into the first energy storage stratification data.

[0121] Performing electrical characteristic stratification processing according to the real-time energy storage operation data to obtain second energy storage stratification data;

[0122] Specifically, the data is layered according to the electrical characteristics. For example, it is classified by voltage level, power state (charging / discharging), current type, etc.: Voltage level: high voltage (≥400V), medium voltage (200-400V), low voltage (<200V). Power state: whether it is currently in charging or discharging state. Calculate parameters of different electrical characteristics categories. For example, for high voltage level equipment, record average temperature, current and other data. Calculate parameters separately in the charging and discharging states, for example, the average voltage in the charging state and the temperature in the discharging state. Combine the electrical characteristics classification data to form the second energy storage layered data.

[0123] Performing time dimension stratification processing according to the real-time operation data of energy storage to obtain third energy storage stratification data;

[0124] Specifically, the data is layered according to time to reflect the time changes of the data. For example: Minute level: real-time data collection, updated every 5 minutes. Hour level: statistical analysis of hourly data to generate hourly mean, maximum, etc. Day level: daily data summary, such as the SOC change trend throughout the day, charge and discharge times, etc. Calculate the average or total value of each time level. For example: calculate the average voltage and temperature at the hourly level. Calculate the total power change at the daily level. Output the third energy storage layer data:

[0125] A hierarchical network is constructed according to the first energy storage hierarchical data, the second energy storage hierarchical data and the third energy storage hierarchical data to obtain energy storage hierarchical data.

[0126] Specifically, data is associated according to device type, electrical characteristics, and time dimensions to build a multi-level data network to enable multi-angle data analysis. Define the connection relationship between different hierarchical data. For example, data of the same device at different time points are associated to form a time series chain. Build an association between device type and electrical characteristics: For example, the voltage and current data of lithium batteries are associated with the charge and discharge status to obtain the electrical characteristics of each device type under different power states. Build an association between the time dimension and device type: For example, the SOC changes of lithium battery types within a day form a time series to facilitate trend prediction.

[0127] Hierarchical Network Table

[0128] Layer Type Related Dimensions Real-time data Device Type Layer Lithium battery, lead-acid battery Real-time SOC and temperature of each device Electrical properties layer High voltage, charging status Voltage and power status Time dimension layer Hourly, daily Hourly and daily SOC trends

[0129] Optionally, the device type hierarchical processing includes:

[0130] Classify the equipment types according to the real-time operation data of energy storage to obtain the classification data of energy storage equipment;

[0131] Specifically, the real-time data of energy storage equipment is classified by device type to identify different device types, such as lithium batteries, lead-acid batteries, etc. The basis for device classification may include device model, installation location, storage capacity, etc. For example: Device A: lithium battery, indoor installation, capacity 300kWh. Device B: lead-acid battery, outdoor installation, capacity 500kWh. The real-time data of each device is associated with its device type to generate device classification data. The real-time data of each device type includes voltage, current, temperature, SOC, etc.

[0132] Extract subsystem data according to energy storage device classification data to obtain energy storage subsystem classification data;

[0133] Specifically, each type of device is further divided into different subsystems based on the configuration and function of the device, such as charging and discharging system, temperature control system, power management system, etc. If both device A and device B have temperature control and power management subsystems, the data is further classified accordingly. The data of each subsystem is extracted and processed separately. For example, the temperature control subsystem extracts temperature data, and the power management system extracts voltage and SOC data.

[0134] Classify the operation characteristics according to the classification data of the energy storage subsystem to obtain the energy storage operation characteristic data;

[0135] Specifically, according to the functions and operating status of different subsystems, the subsystem data is further classified according to the operating characteristics. For example: Temperature control system: classified according to temperature status (such as whether it is over-temperature). Power management system: classified according to charging and discharging status (such as charging, discharging, idle). Generate a subsystem status for each operating characteristic. For example: The temperature status of the temperature control system of device A is normal (<40°C). The power management system of device A is in charging status, SOC 70%.

[0136] Device characteristics are aggregated according to the energy storage operation characteristic data to obtain first energy storage hierarchical data.

[0137] Specifically, data on different operating characteristics are aggregated to form an overview of device characteristics. For example, average voltage, average current, average temperature, and SOC are aggregated for each type of device. The number of states of different subsystems is counted, such as the number of devices in the charging state and the number of devices in the idle state. The operating characteristic data is aggregated by subsystem and device type. For example, the average voltage, current, and temperature of lithium battery devices.

[0138] Optionally, the electrical characteristics layering process includes:

[0139] Electrical parameters are identified according to real-time operation data of energy storage to obtain classification data of electrical parameters of energy storage;

[0140] Specifically, key electrical parameters, such as voltage, current, power, frequency, etc., are identified through real-time data of energy storage devices. For different devices, the electrical parameters are associated with the device ID to generate classified data. The identified data is classified by electrical parameter type. For example, by voltage, current, power, etc.

[0141] Process the voltage and current characteristics according to the energy storage electrical parameter classification data to obtain the energy storage voltage and current distribution data;

[0142] Specifically, the voltage and current data in the electrical parameter classification data are processed for characteristics, including voltage and current distribution, peak value, and fluctuation range. The voltage and current range of each device is calculated, and typical values ​​and abnormal values ​​are determined. The distribution data of voltage and current is generated, including average value, fluctuation range, etc. For example, the voltage distribution of device A is between 410V-420V, and the average current is 120A.

[0143] Perform load balancing according to real-time operation data of energy storage to obtain energy storage load characteristic data;

[0144] Specifically, based on the real-time data of the equipment, analyze the load characteristics, such as whether the current power meets the load demand and whether there is load imbalance. Calculate the ratio of the load level to the maximum load of the equipment to determine whether there is load imbalance. Generate load balance data, including load ratio and load status. For example, the load ratio of equipment A is 80% and the status is balanced.

[0145] Perform power characteristic analysis based on energy storage electrical parameter classification data to obtain energy storage power characteristic data;

[0146] Specifically, the power values ​​in the electrical parameter data are analyzed for characteristics, including average power, peak power, fluctuation range, etc. The change trend of power data, such as peak power and minimum power, is calculated. Power characteristic data is generated. For example, the average power of device A is 49.8kW, and the fluctuation range is between 45-55kW.

[0147] Perform temperature efficiency correlation analysis based on energy storage real-time operation data, energy storage voltage and current distribution data, and energy storage power characteristic data to obtain energy storage temperature efficiency data;

[0148] Specifically, the temperature data is correlated with the voltage, current and power characteristic data to study the impact of temperature changes on device efficiency. It is determined whether the temperature affects the stability of power output and the trend of power output changes with temperature is analyzed. The correlation data of temperature and efficiency is generated. For example, the efficiency of device A is stable at 32°C and decreases at 40°C.

[0149] Electrical characteristics aggregation is performed according to the energy storage electrical parameter classification data, the energy storage voltage and current distribution data, the energy storage load characteristic data, the energy storage power characteristic data, and the energy storage temperature efficiency data to obtain second energy storage hierarchical data;

[0150] Specifically, aggregate data from each layer, including electrical parameter classification, voltage and current distribution, load characteristics, power characteristics, and temperature efficiency data, to form an overall electrical characteristics overview. Generate electrical characteristics overview data for each device to facilitate further scheduling and management. Summarize data on different characteristics, such as the voltage range, current range, load status, power efficiency, etc. of device A.

[0151] The load balancing features include:

[0152] Extract load characteristics based on real-time operation data of energy storage to obtain energy storage load characteristic data;

[0153] Specifically, load characteristic parameters such as power demand, load curve, peak load, etc. are extracted through real-time operation data of energy storage. The load changes of the equipment in different time periods are recorded to determine the daily and hourly load demand change trends. A data table is generated based on the extracted load characteristic parameters. For example, the peak power demand of device A during peak hours (such as 6 pm to 10 pm) is recorded as 60kW, and during low hours (such as 2 am to 5 am) is 20kW.

[0154] Perform load clustering processing according to energy storage load characteristic data to obtain energy storage load clustering data;

[0155] Specifically, the energy storage load characteristic data is clustered, and the equipment is divided into different load types according to the load characteristics, such as high load, medium load, and low load. The equipment is grouped according to the similarity of load demand and similar load patterns are identified. For example, equipment with higher load during peak hours is grouped together.

[0156] Performing graph load characteristic processing according to energy storage load clustering data to obtain energy storage load graph data;

[0157] Specifically, based on the load clustering data, the devices of different load categories and their load relationships are constructed into a graph structure to show the load relationship and dependency between the devices. The graph nodes are used to represent the devices, and the edges represent the load association or load transfer relationship between the devices. For example, high-load device A is connected with device B to show their similarity during peak load. The energy storage load graph data is generated to represent the relationship between the load category devices. For example, devices A and B are connected as similar nodes in the graph to show their load similarity during peak hours.

[0158] Calculate the mixed load balance index according to the energy storage load diagram data to obtain the energy storage load mixed index data;

[0159] Specifically, the mixed load balance index is calculated for the load graph data to measure the degree of balance between different load categories. The load balance index is generated by calculating the load similarity and load fluctuation rate between devices. If the load similarity between devices is high and the load fluctuation is small, the balance index is high. Generate mixed load balance index data for each device or load category. For example, the mixed load balance indexes of devices A and B are 0.85 and 0.80 respectively.

[0160] Specifically, nodes (devices) and edges (load associations between devices) are obtained from the energy storage load graph, and each edge marks the load similarity and load fluctuation rate between devices. The following data is extracted for each device: average load power (unit: kW), load fluctuation range (such as voltage, current, etc.), load similarity (similarity of load patterns between devices, ranging from 0 to 1, 1 means complete similarity). For each pair of devices, their load similarity scores are calculated. Calculated based on the load similarity and load fluctuation rate between devices: S i,j is the load similarity score between devices i and j, is the load similarity between devices i and j, V i,j is the load fluctuation rate between equipment i and j, V max is the maximum load fluctuation rate in the system. The load similarity between A and B is 0.8, the load fluctuation rate is 0.2, and the maximum load fluctuation rate in the system is 1, then S i,j =0.64. The load balance index of each device is the average of the device similarity scores, which reflects the load correlation between the device and other devices. The formula is: B i is the load balance index of the i-th device, j is the device order item, S i,j is the load similarity score between devices i and j, and n is the number of devices. The similarity scores between device A and devices B, C, and D are 0.64, 0.7, and 0.6 respectively, so the load balance index of device A is B A =0.65. The system's mixed load balance index is the average of all equipment load balance indexes, indicating the load coordination degree of the entire energy storage system. Q is the mixed load balance index, i is the equipment sequence item, n is the equipment quantity data, B i is the load balance index of the i-th device. The load balance indexes of devices A, B, C, and D are 0.65, 0.72, 0.68, and 0.55, respectively, so Q = 0.65. The closer the index value is to 1, the higher the load matching degree between system devices is, and the more balanced the load state is. The closer the value is to 0, the more unbalanced the system load is, and there may be a risk of uneven load distribution. Set a threshold, for example, greater than 0.8 means good load balance, 0.5 to 0.8 means average load, and less than 0.5 means unbalanced load, which requires scheduling optimization.

[0161] The energy storage load characteristic data is obtained by performing time series sensitivity processing according to the energy storage load mixed index data.

[0162] Specifically, a time series sensitivity analysis is performed based on the load mixed index data to detect the degree of change of the equipment load in different time periods. The time series fluctuation of the load is identified, and the load with higher sensitivity during peak hours is marked as high time series sensitivity for adaptive scheduling. For example, the load of equipment A varies greatly during peak hours and has higher sensitivity. The energy storage load characteristic data is output, including the classification results of high, medium, and low time series sensitivity. For example, the time series sensitivity of equipment A is high, and that of equipment B is medium.

[0163] Optionally, the time dimension hierarchical processing includes:

[0164] Perform time labeling processing based on the real-time operation data of energy storage to obtain energy storage time label data;

[0165] Specifically, timestamp information is extracted from real-time operation data, and the data is time-tagged so that characteristic analysis can be performed by time period. Time tag data is generated, including date, time, and hour, etc., to distinguish data in different time periods.

[0166] Perform a first time characteristic analysis based on the energy storage time tag data to obtain short-term time characteristic data;

[0167] Specifically, time-tagged data is used to analyze short-term time characteristic data, such as hourly or daily load demand, volatility, etc. Short-term data analysis reflects the changes in energy storage data within an hour or day, and is used to identify instantaneous load demand. Short-term characteristic data is generated, such as the load demand fluctuation from 8:00 to 9:00 in the morning of a certain day.

[0168] Perform a second time characteristic analysis based on the energy storage time tag data to obtain mid-term time characteristic data;

[0169] Specifically, perform mid-term analysis on the time-tagged data, such as weekly load patterns, daily fluctuation trends, etc. Mid-term characteristic data reflects the load demand pattern for multiple days or a week, providing a reference for judging weekly trends. Generate weekly time characteristic data to reflect load change trends. For example, calculate the load difference between weekdays and weekends.

[0170] Perform a third time characteristic analysis based on the energy storage time tag data to obtain long-term time characteristic data, wherein the time data corresponding to the first time characteristic analysis is less than or equal to the time data corresponding to the second time characteristic analysis, and the time data corresponding to the second time characteristic analysis is less than or equal to the time data corresponding to the third time characteristic analysis;

[0171] Specifically, through long-term analysis of time-tagged data, monthly or quarterly load change patterns, such as seasonal fluctuations and monthly peak loads, are studied. Long-term time characteristic analysis helps identify long-term trends and cyclical fluctuations. Monthly or quarterly time characteristic data is generated. For example, the load difference between summer and winter is recorded.

[0172] According to the short-term time characteristic data, the medium-term time characteristic data and the long-term time characteristic data, the peak and valley time periods are segmented to obtain the peak and valley characteristic data;

[0173] Specifically, short-term, medium-term and long-term time characteristic data are used to perform peak-valley period segmentation, dividing the data into peak period and valley period. Peak-valley feature recognition helps optimize load management, such as charging operations during valley periods. Each period is marked as a peak period or valley period. For example, 6 pm to 8 pm is the peak period, and 2 am to 4 am is the valley period.

[0174] The time correlation characteristics of the short-term time characteristic data, the medium-term time characteristic data and the long-term time characteristic data are extracted according to the peak-valley characteristic data to obtain the third energy storage stratification data.

[0175] Specifically, the peak-valley characteristic data is used to correlate and extract the short-term, medium-term and long-term time characteristic data, and the characteristics of different time scales are integrated to identify the correlation of load changes in multiple periods. This analysis can reveal the characteristic correlation at each time scale, which is helpful for the dispatch optimization of energy storage equipment. The time correlation characteristic data of short-term, medium-term, long-term and peak-valley characteristics are integrated to generate the third energy storage stratification data. For example, the changes in the morning and evening peak loads in the short and long term are calculated.

[0176] Optionally, S2 includes:

[0177] S21. Perform digital twin mapping and construction based on rural energy storage data to obtain an initial digital twin model of rural energy storage;

[0178] Specifically, using rural energy storage data as the basis, the actual energy storage system is data mapped to build a virtual model corresponding to the physical system. The model contains parameters such as voltage, current, temperature, SOC (state of charge) and other parameters of the energy storage device. Create a virtual digital space to synchronize and reflect the data status of the physical energy storage system in real time, so that the initial digital twin model has characteristics consistent with the actual system. Modeling is based on the collected data, such as the fluctuation range of voltage and current, and the changing trends of temperature and SOC. The initial digital twin model contains the main state parameters and real-time changing characteristics of each energy storage device.

[0179] S22. Extract energy storage system features based on the initial digital twin model of rural energy storage to obtain energy storage system feature data;

[0180] Specifically, extract the key characteristic parameters of the energy storage system from the digital twin model, including the device's operating mode, charging and discharging status, temperature change rate, etc. Analyze the characteristic data of the device in different states, such as the temperature rise rate under high load and the current change under different voltage levels. Sort out the extracted characteristic data and build a system characteristic table. For example, record the maximum discharge power, charging efficiency, temperature control characteristics, etc. of each device.

[0181] S23, constructing a real-time response layer according to the characteristic data of the energy storage system to obtain real-time response layer data of the energy storage system;

[0182] Specifically, based on the characteristic data of the energy storage system, a real-time response layer of the system is constructed, so that the digital twin model can respond dynamically when receiving real-time data and simulate the performance of the device in different states. Set key characteristic trigger conditions, such as simulating temperature and current changes under high load conditions; under low load conditions, monitor the change trends of SOC and voltage. According to the construction of the real-time response layer, the corresponding data is output. For example, simulate the change of temperature of the device under high load conditions and the change of charging efficiency under low load conditions.

[0183] S24, pairing virtual and real characteristics according to the initial digital twin model of rural energy storage to obtain virtual and real characteristic calibration table data;

[0184] Specifically, the virtual characteristic data in the digital twin model is compared and calibrated with the physical characteristic data of the actual energy storage device item by item to ensure the consistency between the virtual model and the physical system. The matching content includes key characteristics such as voltage, current, SOC and temperature to verify whether the operating modes of the virtual and physical systems are consistent. A matching table of the virtual model and the physical system is generated to show the matching status of the two. For example, the deviation between the virtual voltage and the actual voltage of device A is recorded.

[0185] S25. The initial rural energy storage digital twin model is verified according to the real-time response layer data of the energy storage system and the virtual-real characteristic calibration table data to obtain the rural energy storage digital twin model.

[0186] Specifically, the digital twin initial model is verified using real-time response layer data and virtual-real characteristic calibration table data to ensure the simulation effect and accuracy of the model under different states. By comparing the real-time response data and the actual equipment status data, the real-time response capability and virtual-real matching degree of the model are confirmed, and deviations are identified and corrected. According to the verification results, the parameters and operating characteristics of the digital twin model are adjusted to make it closer to the behavior of the actual energy storage system, forming an energy storage digital twin model. The digital twin model accurately reflects the characteristics of the rural energy storage system, including the response characteristics under different load states and the dynamic changes of key parameters.

[0187] Optionally, S3 includes:

[0188] S31. Extract energy storage status according to the rural energy storage digital twin model to obtain energy storage status data;

[0189] Specifically, key energy storage status data, including current voltage, current, temperature, SOC (state of charge), etc., are extracted from the digital twin model to reflect the operating status of the equipment in real time.

[0190] S32, performing abnormal state detection according to the energy storage state data to obtain energy storage abnormal state data;

[0191] Specifically, set a threshold range (such as voltage, temperature, SOC) to detect abnormal conditions of energy storage devices. If the data exceeds the set range, it is marked as an abnormal state. Detect abnormal conditions such as voltage is too high, temperature is too high, SOC is too low, etc. The detected abnormal data is recorded in the abnormal state table. For example, if the temperature of device A exceeds 40°C, it is marked as a temperature abnormality.

[0192] S33, performing energy storage health assessment according to the energy storage state data and the energy storage abnormal state data to obtain energy storage health data;

[0193] Specifically, the health of the equipment is evaluated based on the energy storage status and abnormal status data. Factors such as the long-term operating stability of the equipment and the frequency of abnormalities are considered during the evaluation. The health can be scored based on the status of each indicator and the health percentage of the equipment is calculated. The health evaluation results are output, for example, the health of equipment A is 85% and that of equipment B is 70%.

[0194] S34, obtaining historical energy storage usage data, and estimating the energy storage life according to the historical energy storage usage data and energy storage health data to obtain energy storage life estimation data;

[0195] Specifically, obtain the historical usage data of the device, including the number of charge and discharge cycles, temperature change records, SOC changes, etc. Use historical usage data and health data to analyze the device life trend and perform life estimation. Estimate the remaining life of the device based on historical and current health. For example, the estimated life of device A is 3 years at the current health.

[0196] S35, calculating energy utilization efficiency according to the energy storage state data to obtain energy storage energy utilization efficiency data;

[0197] Specifically, the energy efficiency of the device is calculated based on the energy storage status data. The effective utilization of energy is measured using factors such as charge and discharge efficiency, temperature stability, and SOC change rate. The energy storage efficiency is calculated using the ratio of the device's output power to input power. The energy efficiency of each device is output. For example, the energy efficiency of device A is 88%.

[0198] S36. Perform real-time risk processing on energy storage life estimation data and energy storage energy efficiency data to obtain rural energy storage status data.

[0199] Specifically, use energy storage life estimation and energy efficiency data to conduct risk management and identify potential risks of equipment and failures that may occur in the short term. Issue early warnings or scheduling suggestions for low-life and low-efficiency equipment to optimize their usage patterns to reduce failure risks. Output rural energy storage status data, which includes the current status of the equipment, risk level, and recommended operating measures. For example, if the remaining life of equipment B is low, it is recommended to reduce high-load operation.

[0200] Optionally, S4 includes:

[0201] S41. Perform load forecasting and demand matching according to rural energy storage status data to obtain load demand matching table data;

[0202] Specifically, based on energy storage status data and historical load data, future load demand is predicted and load trends in different time periods are determined. Load forecasting focuses on load demand during peak and off-peak periods to rationally allocate the resources of energy storage equipment. According to the predicted load demand, the capacity and charging and discharging capabilities of the energy storage equipment are matched with the demand to generate a load demand matching table. Identify time periods with high load demand and ensure that there are enough energy storage equipment to meet the load demand.

[0203] S42, setting a priority strategy according to the load demand matching table data to obtain energy storage scheduling priority plan data;

[0204] Specifically, according to the load demand matching table, the dispatch priority is formulated. The priority strategy can be set according to the urgency of the load demand and the availability of the equipment. The time period with high load demand and insufficient matching has the highest priority, ensuring that more energy storage equipment is dispatched during the peak load period. Generate a priority dispatch plan, mark the priority of each time period, and allocate appropriate energy storage equipment resources. For example, the peak period of 18:00-20:00 is set as the highest priority.

[0205] S43, performing dynamic charging and discharging window optimization according to the energy storage scheduling priority plan data to obtain dynamic charging and discharging schedule data;

[0206] Specifically, the charging and discharging time periods of each device are optimized according to the scheduling priority. Longer discharge windows are allocated to time periods with high priority, while charging windows are allocated to time periods with low load demand, so that energy storage devices can make full use of valley electricity prices and charging opportunities. Set dynamic charging and discharging strategies to ensure high SOC during high load demand periods and low power consumption during low load demand periods. Generate a dynamic charging and discharging schedule to clarify the charging and discharging time periods of each energy storage device. For example, the discharge time period of device A is 18:00-20:00, and the charging time period is 2:00-4:00 in the morning.

[0207] S44. Coordinated dispatch of multiple devices is performed based on dynamic charge and discharge schedule data to obtain rural energy storage dispatch data for auxiliary operations in rural energy storage management.

[0208] Specifically, according to the dynamic charging and discharging schedule, energy storage devices are coordinated and dispatched in different time periods to achieve load balancing and efficient energy management. The charging and discharging status of each device is coordinated to ensure that the device has enough power to meet the load demand during peak hours. When assigning tasks, the health and remaining life of the device are considered, and the health of the device is given priority to extend the life of the system. The coordinated dispatching data of multiple devices is output, marking the specific tasks of each device in each time period, such as discharging during peak hours and charging during off-peak hours.

[0209] Optionally, the present application also provides a rural digital twin energy storage management system based on the Internet of Things, which is used to execute the rural digital twin energy storage management method based on the Internet of Things as described above, and the rural digital twin energy storage management system based on the Internet of Things includes:

[0210] Rural energy storage real-time collection module, used to collect real-time operation data through rural distributed energy storage equipment to obtain rural energy storage data;

[0211] A digital twin model construction module is used to construct a digital twin model based on rural energy storage data to obtain a rural energy storage digital twin model;

[0212] The energy storage status detection module is used to detect the energy storage status according to the rural energy storage digital twin model to obtain rural energy storage status data;

[0213] The rural energy storage dispatching module is used to optimize dispatching according to rural energy storage status data and obtain rural energy storage dispatching data to assist in rural energy storage management operations.

[0214] Therefore, from any point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the attached application documents rather than the above description, and it is intended that all changes falling within the meaning and scope of equivalent elements of the application documents are included in the present invention.

[0215] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A rural digital twin energy storage management method based on the Internet of Things, characterized in that: The method comprises: S1. Collect real-time operation data through rural distributed energy storage equipment to obtain rural energy storage data; S2. Construct a digital twin model based on rural energy storage data to obtain a rural energy storage digital twin model; S3. Perform energy storage status detection based on the rural energy storage digital twin model to obtain rural energy storage status data; S4. Optimize the scheduling based on the rural energy storage status data to obtain rural energy storage scheduling data to assist in rural energy storage management operations.

2. The method according to claim 1, characterized in that S1 includes: Collect real-time operation data through rural distributed energy storage equipment to obtain real-time energy storage operation data; Perform hierarchical processing according to the real-time operation data of energy storage to obtain energy storage hierarchical data; Data synchronization is performed based on the energy storage stratification data to obtain rural energy storage data.

3. The method according to claim 2, characterized in that The layered processing includes: Performing device type stratification processing according to the real-time energy storage operation data to obtain first energy storage stratification data; Performing electrical characteristic stratification processing according to the real-time energy storage operation data to obtain second energy storage stratification data; Performing time dimension stratification processing according to the real-time operation data of energy storage to obtain third energy storage stratification data; A hierarchical network is constructed according to the first energy storage hierarchical data, the second energy storage hierarchical data and the third energy storage hierarchical data to obtain energy storage hierarchical data.

4. The method according to claim 3, characterized in that The device type hierarchical processing includes: Classify the equipment types according to the real-time operation data of energy storage to obtain the classification data of energy storage equipment; Extract subsystem data according to energy storage device classification data to obtain energy storage subsystem classification data; Classify the operation characteristics according to the classification data of the energy storage subsystem to obtain the energy storage operation characteristic data; Device characteristics are aggregated according to the energy storage operation characteristic data to obtain first energy storage hierarchical data.

5. The method according to claim 3, characterized in that: The electrical characteristics layering process includes: Electrical parameters are identified according to real-time operation data of energy storage to obtain classification data of energy storage electrical parameters; Process the voltage and current characteristics according to the energy storage electrical parameter classification data to obtain the energy storage voltage and current distribution data; Perform load balancing according to real-time operation data of energy storage to obtain energy storage load characteristic data; Perform power characteristic analysis based on energy storage electrical parameter classification data to obtain energy storage power characteristic data; Perform temperature efficiency correlation analysis based on energy storage real-time operation data, energy storage voltage and current distribution data, and energy storage power characteristic data to obtain energy storage temperature efficiency data; Electrical characteristics aggregation is performed according to the energy storage electrical parameter classification data, the energy storage voltage and current distribution data, the energy storage load characteristic data, the energy storage power characteristic data, and the energy storage temperature efficiency data to obtain second energy storage hierarchical data; The load balancing features include: Extract load characteristics based on real-time operation data of energy storage to obtain energy storage load characteristic data; Perform load clustering processing according to energy storage load characteristic data to obtain energy storage load clustering data; Performing graph load characteristic processing according to energy storage load clustering data to obtain energy storage load graph data; Calculate the mixed load balance index according to the energy storage load diagram data to obtain the energy storage load mixed index data; The energy storage load characteristic data is obtained by performing time series sensitivity processing according to the energy storage load mixed index data.

6. The method according to claim 3, characterized in that The time dimension layered processing includes: Perform time labeling processing based on the real-time operation data of energy storage to obtain energy storage time label data; Perform a first time characteristic analysis based on the energy storage time tag data to obtain short-term time characteristic data; Perform a second time characteristic analysis based on the energy storage time tag data to obtain mid-term time characteristic data; Perform a third time characteristic analysis based on the energy storage time tag data to obtain long-term time characteristic data, wherein the time data corresponding to the first time characteristic analysis is less than or equal to the time data corresponding to the second time characteristic analysis, and the time data corresponding to the second time characteristic analysis is less than or equal to the time data corresponding to the third time characteristic analysis; According to the short-term time characteristic data, the medium-term time characteristic data and the long-term time characteristic data, the peak and valley time periods are segmented to obtain the peak and valley characteristic data; The time correlation characteristics of the short-term time characteristic data, the medium-term time characteristic data and the long-term time characteristic data are extracted according to the peak-valley characteristic data to obtain the third energy storage stratification data.

7. The method according to claim 1, characterized in that S2 include: Digital twin mapping is constructed based on rural energy storage data to obtain the initial digital twin model of rural energy storage; Extract energy storage system features based on the initial digital twin model of rural energy storage to obtain energy storage system feature data; Construct a real-time response layer according to the characteristic data of the energy storage system to obtain the real-time response layer data of the energy storage system; According to the initial digital twin model of rural energy storage, virtual and physical characteristics are matched to obtain virtual and real characteristic calibration table data; The initial digital twin model of rural energy storage is verified based on the real-time response layer data of the energy storage system and the virtual-real characteristic calibration table data to obtain the rural energy storage digital twin model.

8. The method according to claim 1, characterized in that S3 include: Extract energy storage status based on the rural energy storage digital twin model to obtain energy storage status data; Perform abnormal state detection according to the energy storage state data to obtain energy storage abnormal state data; Perform energy storage health assessment based on energy storage status data and energy storage abnormal status data to obtain energy storage health data; Obtain historical energy storage usage data, and estimate the energy storage life based on the historical energy storage usage data and energy storage health data to obtain energy storage life estimation data; Calculate energy utilization efficiency based on energy storage status data to obtain energy storage energy utilization efficiency data; Real-time risk processing is performed on energy storage life estimation data and energy storage energy utilization efficiency data to obtain rural energy storage status data.

9. The method according to claim 1, characterized in that S4 include: Carry out load forecasting and demand matching based on rural energy storage status data to obtain load demand matching table data; Priority strategy is set according to the load demand matching table data to obtain energy storage dispatch priority plan data; Perform dynamic charging and discharging window optimization based on energy storage dispatch priority plan data to obtain dynamic charging and discharging schedule data; Based on the dynamic charging and discharging schedule data, multi-device coordinated scheduling is carried out to obtain rural energy storage scheduling data for auxiliary operations in rural energy storage management.

10. A rural digital twin energy storage management system based on the Internet of Things, characterized in that: Used to execute the rural digital twin energy storage management method based on the Internet of Things as claimed in claim 1, the rural digital twin energy storage management system based on the Internet of Things includes: Rural energy storage real-time collection module, used to collect real-time operation data through rural distributed energy storage equipment to obtain rural energy storage data; A digital twin model construction module is used to construct a digital twin model based on rural energy storage data to obtain a rural energy storage digital twin model; The energy storage status detection module is used to detect the energy storage status according to the rural energy storage digital twin model to obtain rural energy storage status data; The rural energy storage dispatching module is used to optimize dispatching according to rural energy storage status data and obtain rural energy storage dispatching data to assist in rural energy storage management operations.

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