Energy storage equipment power balance dynamic load automatic control method and system

Abstract

The present invention discloses a method and system for automatic control of dynamic load for power balance of energy storage equipment, including: constructing a charging power optimization model based on the electricity price policy divided into peak and valley periods, combined with historical electricity demand data, to obtain the optimal charging power in each time period; constructing a discharge power dynamic adjustment model based on the operating status parameters obtained by the real-time environmental parameter acquisition device, combined with real-time electricity demand data, to obtain the optimal discharge power in each time period; optimizing the two model parameters, balancing the load state based on the optimal charge and discharge power; predicting overload conditions under abnormal factors based on historical control data; and based on power imbalance conditions, prioritizing the power supply to critical loads and performing energy storage equipment status self-checking according to the power allocation priority. The advantages of the present invention are: combining electricity prices with real-time demand, optimizing charging power and discharging power, thereby balancing the load state, reducing energy costs and avoiding the risk of equipment overload.

Description

Technical Field

[0001] The present invention relates to load regulation technology, and in particular to a method and system for automatically regulating power balance dynamic load of energy storage equipment. Background Art

[0002] With the rapid advancement of clean energy and the continued expansion of power grids, energy storage has become a key element in ensuring the sustainable use of green energy. Energy storage devices, such as electrochemical energy storage, play a key role in regulating power supply and demand, optimizing energy efficiency, and maintaining reliable grid operation within transmission and distribution networks. However, due to the inherent volatility and randomness of natural energy sources such as wind and solar power, energy storage units face multi-faceted operational and management challenges when managing dynamic loads.

[0003] Most of the current power load control methods for energy storage devices on the market are based on preset power allocation strategies, typically relying on simple time period divisions or fixed charging and discharging patterns to adjust the load. These methods typically respond to varying load demands by setting fixed charging and discharging power values, but this static control method struggles to adapt to complex and dynamic electricity demands. Many methods fail to fully account for the differences in peak and off-peak electricity prices on the power grid, and unnecessary charging or discharging continues during peak hours, resulting in energy waste and increased costs. Traditional energy storage devices lack the flexibility to adjust power based on real-time environmental parameters (such as temperature and load changes). As a result, the system cannot respond in a timely manner when the load fluctuates significantly, potentially leading to overload or energy efficiency loss. Summary of the Invention

[0004] In order to improve the existing power load control methods and systems for energy storage equipment, a method and system for automatic control of the dynamic load of energy storage equipment power balance is provided. This method optimizes the charging and discharging power to ensure that power resources are fully utilized in different time periods, effectively reducing electricity prices and costs, and balancing the load status when power demand fluctuates. By precisely controlling the charging and discharging power, the system can achieve efficient energy utilization and optimized scheduling while ensuring power supply to critical loads.

[0005] In order to achieve the above objects, the technical solution adopted by the present invention is:

[0006] The method for automatic control of dynamic load of power balance of energy storage equipment includes:

[0007] Construct a bidirectional power coupling interface for energy storage devices;

[0008] Based on the electricity price policy divided into peak and valley periods and combined with historical electricity demand data, a charging power optimization model is constructed to obtain the charging power in each time period;

[0009] The operating status parameters obtained by the real-time environmental parameter acquisition device are combined with the real-time power demand data to build a dynamic discharge power adjustment model to obtain the discharge power in each time period;

[0010] Based on the charging power optimization model and the discharging power dynamic adjustment model, the two model parameters are optimized to balance the load state based on the optimal charging and discharging power;

[0011] Based on historical control data, overload conditions under abnormal factors are predicted and compared with real-time monitoring data;

[0012] Based on the power imbalance, the system prioritizes power distribution to ensure power supply to critical loads and performs self-checks on the energy storage device status.

[0013] Preferably, the electricity price policy based on peak and valley time periods is combined with historical electricity demand data to construct a charging power optimization model, and obtaining the charging power in each time period specifically includes:

[0014] Obtain the electricity price policy in the region where the energy storage equipment is located and construct a piecewise linear electricity price function in the time dimension;

[0015] Obtain the dynamic charging power fluctuation range based on the available power of the grid, the current acceptable charging power of the energy storage device, and the safety constraint power;

[0016] Obtain electricity demand data for different periods based on historical data;

[0017] Based on the electricity demand data obtained in different time periods, the charging power function of the different time periods is obtained;

[0018] Based on the constructed piecewise linear electricity price function and the charging power function at different time periods, a charging power optimization model is constructed to calculate the minimum electricity fee by minimizing the objective function;

[0019] Based on the minimization calculation of the objective function value, the charging power in each time period is obtained while minimizing the electricity cost.

[0020] Preferably, the operating status parameters obtained by the real-time environmental parameter acquisition device are combined with the real-time power demand data to construct a dynamic adjustment model for discharge power, and obtaining the discharge power in each time period specifically includes:

[0021] Obtain the surface temperature distribution of the energy storage device through the temperature sensor array and calculate the temperature gradient field strength;

[0022] Calculate the environmental humidity impact factor through humidity sensor data;

[0023] Obtain real-time electricity demand data through ammeters;

[0024] Based on the acquired real-time electricity demand data, a real-time charging power function is obtained;

[0025] Based on the acquired real-time operating status parameter data, a dynamic adjustment model for discharge power is constructed to correct the real-time charging power function and obtain the discharge power in each time period.

[0026] Preferably, the optimizing of the two model parameters based on the charging power optimization model and the discharging power dynamic adjustment model and balancing the load state based on the optimal charging and discharging power specifically include:

[0027] Based on the charging power optimization model and the discharging power dynamic adjustment model, the charging power and discharging power in each time period are obtained, and the average load of the energy storage device in each time period is obtained;

[0028] By adjusting the start time of charging and discharging, and the power of charging and discharging, several groups of adjustment sample data are obtained;

[0029] By adjusting the sample data to obtain the load conditions of the energy storage device under various conditions, the sample parameter data with a value less than the load mean is obtained;

[0030] Based on the sample parameter data that is less than the load average, the corresponding electricity fee data is calculated;

[0031] By assigning weights to the energy storage device load and electricity charges, the sample parameter data with the lowest score is calculated and obtained, and the optimal charge and discharge power is obtained based on the sample parameter data.

[0032] Preferably, the predicting of overload conditions under abnormal factors based on historical control data and comparing with real-time monitoring data specifically includes:

[0033] Based on historical control data, obtain charging and discharging power overload data during large-scale events and holidays;

[0034] Based on overload data, feature extraction is performed to obtain the changing trend of energy storage equipment power demand before and after overload, and an overload prediction model is constructed;

[0035] The energy storage device operating data acquired through real-time detection is substituted into the overload prediction model. Dynamic time warping is used to match the data with the power demand change trend data before the overload occurs. The real-time window data is aligned with the historical window data before the overload in a time series, and the DTW distance is calculated to obtain the similarity of the change trends.

[0036] Based on the obtained similarity of the change trend, a confidence level of the similarity is calculated, and based on the confidence level, whether the current energy storage device will be overloaded is determined;

[0037] If the prediction result conflicts with the actual result, the overload prediction model is reconstructed, and the prediction result is regenerated using the erroneous parameter model, and the parameter model is used as the key training parameter.

[0038] Preferably, the step of prioritizing power supply to critical loads and performing energy storage device status self-check based on power imbalance conditions according to power allocation priorities specifically includes:

[0039] Based on the power imbalance in overload conditions, power supply to critical loads is prioritized according to power allocation priorities;

[0040] The power allocation priorities are specifically:

[0041] The first priority is ensuring power supply to critical loads, the second is the self-sustaining power of the energy storage system, the third is grid feedback power regulation, and the fourth is the scheduling of backup energy storage units.

[0042] The performing of the energy storage device status self-check specifically includes:

[0043] Check the remaining power of the energy storage device to assess whether it can support the load demand;

[0044] Detect the charge and discharge status of the energy storage device to confirm whether the device is working properly;

[0045] Evaluate the health status of energy storage devices, including battery aging, battery voltage, internal resistance and other parameters;

[0046] Based on the self-test results, the system dynamically adjusts the charging and discharging strategies of the energy storage equipment to ensure power supply to priority loads.

[0047] Furthermore, a dynamic load automatic control system for power balance of energy storage equipment is proposed, including:

[0048] Distributed sensors: Real-time distributed sensors are mainly used to obtain various status data during the operation of energy storage equipment;

[0049] Charging module: The charging module is mainly used to obtain electrical energy from the power grid;

[0050] Discharge module: The discharge module is mainly used to provide power to the user end;

[0051] Bidirectional power coupling interface: The bidirectional power coupling interface is mainly used to achieve physical layer decoupling of the charging module and the discharging module, allowing the charging circuit and the discharging circuit to operate independently and simultaneously during all weather periods;

[0052] Model construction module: The model construction module is mainly used to build a charging power optimization model, a discharge power dynamic adjustment model, and an overload prediction model;

[0053] Optimization module: The optimization module is mainly used to dynamically adjust the load conditions in each time period based on the optimal charging power and the optimal discharging power;

[0054] Overload prediction module: The overload prediction module is mainly used to compare real-time operation data based on historical control data to predict the trend of overload;

[0055] Status self-check module: The status self-check module is mainly used to perform self-check on the energy storage device after an overload occurs;

[0056] Database module: The database module is mainly used to store the operating status data of the energy storage equipment, power overload abnormality data, etc.;

[0057] Processor: The processor is mainly used for the calculation process of each formula and the construction calculation process of each model.

[0058] Compared with the prior art, the advantages of the present invention are:

[0059] By establishing a bidirectional power coupling interface, the two systems can operate independently around the clock, improving the flexibility and stability of the energy storage system. Supported by a charging power optimization model, combined with peak and valley electricity pricing policies and historical electricity demand data, the charging power for each time period can be accurately calculated, effectively reducing energy costs and improving the system's charging efficiency. Furthermore, by integrating real-time environmental parameter acquisition with electricity demand data, a dynamic discharge power adjustment model is constructed, enabling precise regulation of discharge power and ensuring output power under varying load conditions. By optimizing charge and discharge power, load conditions are balanced, reducing energy waste, and ensuring efficient operation of the energy storage system while avoiding equipment overloads caused by power imbalances. This approach not only predicts and promptly addresses overload risks caused by abnormal factors but also prioritizes power supply to critical loads in the event of power imbalances, improving system reliability and safety. The introduction of a self-checking function for energy storage device status also provides real-time support for system maintenance, ensuring the stability and sustainable operation of the energy storage system under various operating conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 A schematic diagram of the method proposed in the present invention;

[0061] Figure 2 This is a schematic diagram of obtaining charging power proposed by the present invention;

[0062] Figure 3 This is a schematic diagram of obtaining discharge power proposed by the present invention;

[0063] Figure 4 This is a schematic diagram of the load balancing proposed by the present invention;

[0064] Figure 5 This is a schematic diagram of overload prediction proposed by the present invention;

[0065] Figure 6 This is a schematic diagram of the imbalance control and self-checking proposed by the present invention;

[0066] Figure 7 This is a diagram of the architecture of the electronic equipment in this solution;

[0067] Figure 8 This is a schematic diagram of the computer-readable storage medium structure in this solution. DETAILED DESCRIPTION

[0068] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0069] Energy storage equipment power balance dynamic load automatic control system, including:

[0070] Distributed sensors: Real-time distributed sensors are mainly used to obtain various status data during the operation of energy storage equipment;

[0071] Charging module: The charging module is mainly used to obtain electrical energy from the power grid;

[0072] Discharge module: The discharge module is mainly used to provide power to the user end;

[0073] Bidirectional power coupling interface: The bidirectional power coupling interface is mainly used to achieve physical layer decoupling of the charging module and the discharging module, allowing the charging circuit and the discharging circuit to operate independently and simultaneously during all weather periods;

[0074] Model construction module: The model construction module is mainly used to build a charging power optimization model, a discharge power dynamic adjustment model, and an overload prediction model;

[0075] Optimization module: The optimization module is mainly used to dynamically adjust the load conditions in each time period based on the optimal charging power and the optimal discharging power;

[0076] Overload prediction module: The overload prediction module is mainly used to compare real-time operation data based on historical control data to predict the trend of overload;

[0077] Status self-check module: The status self-check module is mainly used to perform self-check on the energy storage device after an overload occurs;

[0078] Database module: The database module is mainly used to store the operating status data of the energy storage equipment, power overload abnormality data, etc.;

[0079] Processor: The processor is mainly used for the calculation process of each formula and the construction calculation process of each model.

[0080] See Figure 1 As shown, the method for automatically controlling the power balance dynamic load of an energy storage device includes:

[0081] Step 1: Build a bidirectional power coupling interface for energy storage devices;

[0082] Step 2: Based on the electricity price policy divided into peak and valley periods and combined with historical electricity demand data, a charging power optimization model is constructed to obtain the charging power in each time period;

[0083] Step 3: Using the operating status parameters acquired by the real-time environmental parameter acquisition device and the real-time power demand data, a dynamic discharge power adjustment model is constructed to obtain the discharge power in each time period.

[0084] Step 4: Based on the charging power optimization model and the discharging power dynamic adjustment model, optimize the two model parameters and balance the load state based on the optimal charging and discharging power;

[0085] Step 5: Based on historical control data, predict overload conditions under abnormal factors and compare them with real-time monitoring data;

[0086] Step 6: Based on the power imbalance, prioritize power supply to critical loads and perform energy storage device status self-checks according to power allocation priorities.

[0087] See Figure 2 As shown in the figure, based on the electricity price policy divided into peak and valley periods and combined with historical electricity demand data, a charging power optimization model is constructed to obtain the charging power in each time period. Specifically, the following are included:

[0088] Obtain the electricity price policy in the region where the energy storage equipment is located and construct a piecewise linear electricity price function in the time dimension;

[0089] Obtain the dynamic charging power fluctuation range based on the available power of the grid, the current acceptable charging power of the energy storage device, and the safety constraint power;

[0090] Obtain electricity demand data for different periods based on historical data;

[0091] Based on the electricity demand data obtained in different time periods, the charging power function of the different time periods is obtained;

[0092] Based on the constructed piecewise linear electricity price function and the charging power function at different time periods, a charging power optimization model is constructed to calculate the minimum electricity fee by minimizing the objective function;

[0093] Based on the minimization calculation of the objective function value, the charging power in each time period is obtained while minimizing the electricity cost.

[0094] Understandably, the grid's power supply capacity may fluctuate due to regional differences, seasonal changes, or unexpected events, resulting in the system being unable to continuously provide stable power. To address this issue, a dynamic adjustment mechanism can be designed to dynamically adjust charging power based on the grid's real-time load conditions (for example, real-time load monitoring data). When the grid's power supply capacity is low, charging power can be automatically reduced to ensure grid stability.

[0095] See Figure 3 As shown, the operating status parameters obtained by the real-time environmental parameter acquisition device are combined with the real-time power demand data to build a dynamic adjustment model for discharge power. The discharge power in each time period is obtained specifically including:

[0096] Obtain the surface temperature distribution of the energy storage device through the temperature sensor array and calculate the temperature gradient field strength;

[0097] Calculate the environmental humidity impact factor through humidity sensor data;

[0098] Obtain real-time electricity demand data through ammeters;

[0099] Based on the acquired real-time electricity demand data, a real-time charging power function is obtained;

[0100] Based on the acquired real-time operating status parameter data, a dynamic adjustment model for discharge power is constructed to correct the real-time charging power function and obtain the discharge power in each time period.

[0101] Understandably, when ambient humidity deviates from the optimal operating level, air thermal conductivity and convective heat transfer capacity change significantly. In high-humidity environments, moisture adsorption can exacerbate insulation degradation on equipment surfaces, while low humidity can lead to the risk of static electricity accumulation. Therefore, it's necessary to factor humidity into the dynamic correction of the temperature rise range, using humidity to adjust the temperature rise tolerance in real time, achieving dual safety margin control.

[0102] See Figure 4 As shown, based on the charging power optimization model and the discharging power dynamic adjustment model, the two model parameters are optimized, and the load state based on the optimal charging and discharging power is balanced. Specifically, the following are included:

[0103] Based on the charging power optimization model and the discharging power dynamic adjustment model, the charging power and discharging power in each time period are obtained, and the average load of the energy storage device in each time period is obtained;

[0104] By adjusting the start time of charging and discharging, and the power of charging and discharging, several groups of adjustment sample data are obtained;

[0105] By adjusting the sample data to obtain the load conditions of the energy storage device under various conditions, the sample parameter data with a value less than the load mean is obtained;

[0106] Based on the sample parameter data that is less than the load average, the corresponding electricity fee data is calculated;

[0107] By assigning weights to the energy storage device load and electricity charges, the sample parameter data with the lowest score is calculated and obtained, and the optimal charge and discharge power is obtained based on the sample parameter data.

[0108] See Figure 5 As shown in the figure, based on historical control data, overload conditions under abnormal factors are predicted and compared with real-time monitoring data. Specifically, the following are included:

[0109] Based on historical control data, obtain charging and discharging power overload data during large-scale events and holidays;

[0110] Based on overload data, feature extraction is performed to obtain the changing trend of energy storage equipment power demand before and after overload, and an overload prediction model is constructed;

[0111] The energy storage device operating data acquired through real-time detection is substituted into the overload prediction model. Dynamic time warping is used to match the data with the power demand change trend data before the overload occurs. The real-time window data is aligned with the historical window data before the overload in a time series, and the DTW distance is calculated to obtain the similarity of the change trends.

[0112] Based on the obtained similarity of the change trend, a confidence level of the similarity is calculated, and based on the confidence level, whether the current energy storage device will be overloaded is determined;

[0113] If the prediction result conflicts with the actual result, the overload prediction model is reconstructed, and the prediction result is regenerated using the erroneous parameter model, and the parameter model is used as the key training parameter.

[0114] Specifically, when constructing an overload prediction model, feature extraction needs to take into account the spatiotemporal correlation of historical overload events and the multi-dimensional coupling characteristics of the equipment operating status.

[0115] See Figure 6As shown, based on power imbalance, power supply to critical loads is prioritized according to power allocation priorities, and energy storage equipment self-checks are performed. Specifically, these include extracting external features such as the date tags, event scale (e.g., crowd density, regional electricity consumption increase), and duration of major events / holidays, mapping them into potential influencing factors for power load. Dynamic features such as statistics (mean, variance, and skewness), frequency domain energy distribution (using wavelet decomposition to determine the proportion of high-frequency fluctuation energy), and power change rate are extracted from charge and discharge power time series data. Multidimensional cross-features are then constructed by combining grid-side data with energy storage equipment parameters.

[0116] Based on the power imbalance in overload conditions, power supply to critical loads is prioritized according to power allocation priorities;

[0117] The power allocation priorities are specifically:

[0118] The first priority is ensuring power supply to critical loads, the second is the self-sustaining power of the energy storage system, the third is grid feedback power regulation, and the fourth is the scheduling of backup energy storage units.

[0119] The performing of the energy storage device status self-check specifically includes:

[0120] Check the remaining power of the energy storage device to assess whether it can support the load demand;

[0121] Detect the charge and discharge status of the energy storage device to confirm whether the device is working properly;

[0122] Evaluate the health status of energy storage devices, including battery aging, battery voltage, internal resistance and other parameters;

[0123] Based on the self-test results, the system dynamically adjusts the charging and discharging strategies of the energy storage equipment to ensure power supply to priority loads.

[0124] Furthermore, the method according to the embodiment of the present application can also be used with the aid of Figure 7 The electronic device architecture shown in FIG. Figure 7 As shown, the electronic device 500 may include a bus 501, one or more CPUs 502, a read-only memory (ROM) 503, a random access memory (RAM) 504, a communication port 505 connected to a network, an input / output component 506, a hard disk 507, etc. The storage device in the electronic device 500, such as the ROM 503 or the hard disk 507, may store the energy storage device power balancing dynamic load automatic control method and system provided in this application. The electronic device 500 may also include a terminal interface 508. Of course, Figure 7 The architecture shown is only exemplary and can be omitted according to actual needs when implementing different devices. Figure 7 One or more components of an electronic device are shown.

[0125] Figure 8 This is a schematic diagram of the computer-readable storage medium structure provided by an embodiment of the present application. Figure 8 As shown, a computer-readable storage medium 600 according to an embodiment of the present application is shown. Computer-readable instructions are stored on the computer-readable storage medium 600. When the computer-readable instructions are executed by the processor, the method and system for automatic control of dynamic load of power balance of energy storage equipment according to the embodiment of the present application described with reference to the above figures can be executed. The storage medium 600 includes, but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0126] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0127] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0128] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for automatically controlling the power balance dynamic load of an energy storage device, characterized in that: include: Construct a bidirectional power coupling interface for energy storage devices; Based on the electricity price policy divided into peak and valley periods and combined with historical electricity demand data, a charging power optimization model is constructed to obtain the charging power in each time period; The operating status parameters obtained by the real-time environmental parameter acquisition device are combined with the real-time power demand data to build a dynamic discharge power adjustment model to obtain the discharge power in each time period; Based on the charging power optimization model and the discharging power dynamic adjustment model, the two model parameters are optimized to balance the load state based on the optimal charging and discharging power; Based on historical control data, predict overload conditions under abnormal factors and compare them with real-time monitoring data; Based on power imbalance, prioritize power distribution to ensure critical loads are powered and perform energy storage device status self-checks. The prediction of overload conditions under abnormal factors based on historical control data and comparison with real-time monitoring data specifically include: Based on historical control data, obtain charging and discharging power overload data during large-scale events and holidays; Based on overload data, feature extraction is performed to obtain the changing trend of energy storage equipment power demand before and after overload, and an overload prediction model is constructed; The energy storage device operating data acquired through real-time detection is substituted into the overload prediction model. Dynamic time warping is used to match the data with the power demand change trend data before the overload occurs. The real-time window data is aligned with the historical window data before the overload in a time series, and the DTW distance is calculated to obtain the similarity of the change trends. Based on the obtained similarity of the change trend, a confidence level of the similarity is calculated, and based on the confidence level, whether the current energy storage device will be overloaded is determined; If the prediction result conflicts with the actual result, the overload prediction model is reconstructed, and the prediction result is regenerated using the erroneous parameter model, and the parameter model is used as the key training parameter.

2. The method for automatic control of power balance dynamic load of energy storage equipment according to claim 1, characterized in that: The electricity price policy based on peak and valley time periods is combined with historical electricity demand data to build a charging power optimization model. The charging power in each time period is obtained specifically including: Obtain the electricity price policy in the region where the energy storage equipment is located and construct a piecewise linear electricity price function in the time dimension; Obtain the dynamic charging power fluctuation range based on the available power of the grid, the current acceptable charging power of the energy storage device, and the safety constraint power; Obtain electricity demand data for different periods based on historical data; Based on the electricity demand data obtained in different time periods, the charging power function of the different time periods is obtained; Based on the constructed piecewise linear electricity price function and the charging power function at different time periods, a charging power optimization model is constructed to calculate the minimum electricity fee by minimizing the objective function; Based on the minimization calculation of the objective function value, the charging power in each time period is obtained while minimizing the electricity cost.

3. The method for automatic control of power balance dynamic load of energy storage equipment according to claim 1, characterized in that: The operating status parameters obtained by the real-time environmental parameter acquisition device are combined with the real-time power demand data to construct a dynamic adjustment model for discharge power. The discharge power in each time period is obtained specifically including: Obtain the surface temperature distribution of the energy storage device through the temperature sensor array and calculate the temperature gradient field strength; Calculate the environmental humidity impact factor through humidity sensor data; Obtain real-time electricity demand data through ammeters; Based on the acquired real-time electricity demand data, a real-time charging power function is obtained; Based on the acquired real-time operating status parameter data, a dynamic adjustment model for discharge power is constructed to correct the real-time charging power function and obtain the discharge power in each time period.

4. The method for automatic control of power balance dynamic load of energy storage equipment according to claim 1, characterized in that: The optimization of the charging power optimization model and the discharging power dynamic adjustment model to optimize the two model parameters and balance the load state based on the optimal charging and discharging power specifically includes: Based on the charging power optimization model and the discharging power dynamic adjustment model, the charging power and discharging power in each time period are obtained, and the average load of the energy storage device in each time period is obtained; By adjusting the start time of charging and discharging, and the power of charging and discharging, several groups of adjustment sample data are obtained; By adjusting the sample data to obtain the load conditions of the energy storage device under various conditions, the sample parameter data with a value less than the load mean is obtained; Based on the sample parameter data that is less than the load average, the corresponding electricity fee data is calculated; By assigning weights to the energy storage device load and electricity charges, the sample parameter data with the lowest score is calculated and obtained, and the optimal charging and discharging power is obtained based on the sample parameter data.

5. The method for automatic control of power balance dynamic load of energy storage equipment according to claim 1, characterized in that: The method of prioritizing power supply to critical loads and performing energy storage device status self-check based on power imbalance according to power allocation priority specifically includes: Based on the power imbalance in overload conditions, power supply to critical loads is prioritized according to power allocation priorities; The power allocation priorities are specifically: The first priority is ensuring power supply to critical loads, the second is the self-sustaining power of the energy storage system, the third is grid feedback power regulation, and the fourth is the scheduling of backup energy storage units. The performing of the energy storage device status self-check specifically includes: Check the remaining power of the energy storage device to assess whether it can support the load demand; Detect the charge and discharge status of the energy storage device to confirm whether the device is working properly; Evaluate the health status of energy storage devices, including battery aging, battery voltage, internal resistance and other parameters; Based on the self-test results, the system dynamically adjusts the charging and discharging strategies of the energy storage equipment to ensure power supply to priority loads.

6. Combined with the energy storage device power balance dynamic load automatic control method, it is used to realize the energy storage device power balance dynamic load automatic control system according to any one of claims 1 to 5, characterized in that: include: Distributed sensors: Real-time distributed sensors are mainly used to obtain various status data during the operation of energy storage equipment; Charging module: The charging module is mainly used to obtain electrical energy from the power grid; Discharge module: The discharge module is mainly used to provide power to the user end; Bidirectional power coupling interface: The bidirectional power coupling interface is mainly used to achieve physical layer decoupling of the charging module and the discharging module, allowing the charging circuit and the discharging circuit to operate independently and simultaneously during all weather periods; Model construction module: The model construction module is mainly used to build a charging power optimization model, a discharge power dynamic adjustment model, and an overload prediction model; Optimization module: The optimization module is mainly used to dynamically adjust the load conditions in each time period based on the optimal charging power and the optimal discharging power; Overload prediction module: The overload prediction module is mainly used to compare real-time operation data based on historical control data to predict the trend of overload; Status self-check module: The status self-check module is mainly used to perform self-check on the energy storage device after an overload occurs; Database module: The database module is mainly used to store the operating status data of the energy storage equipment, power overload abnormality data, etc.; Processor: The processor is mainly used for the calculation process of each formula and the construction calculation process of each model; The prediction of overload conditions under abnormal factors based on historical control data and comparison with real-time monitoring data specifically include: Based on historical control data, obtain charging and discharging power overload data during large-scale events and holidays; Based on overload data, feature extraction is performed to obtain the changing trend of energy storage equipment power demand before and after overload, and an overload prediction model is constructed; The energy storage device operating data acquired through real-time detection is substituted into the overload prediction model. Dynamic time warping is used to match the data with the power demand change trend data before the overload occurs. The real-time window data is aligned with the historical window data before the overload in a time series, and the DTW distance is calculated to obtain the similarity of the change trends. Based on the obtained similarity of the change trend, a confidence level of the similarity is calculated, and based on the confidence level, whether the current energy storage device will be overloaded is determined; If the prediction result conflicts with the actual result, the overload prediction model is reconstructed, and the prediction result is regenerated using the erroneous parameter model, and the parameter model is used as the key training parameter.

7. An electronic device, characterized in that: include: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the energy storage device power balance dynamic load automatic control method as described in any one of claims 1-6.

8. A computer-readable storage medium storing computer-readable instructions, characterized in that: When the computer-readable instructions are executed by a processor, the method for automatic control of power balance dynamic load of an energy storage device according to any one of claims 1 to 6 is implemented.

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