Data processing system for monitoring energy storage of power grid

Abstract

The invention discloses a data processing system for monitoring energy storage of a power grid. The data processing system comprises a battery energy storage system, a data acquisition module, a data processing module and a control module, in the process of generating the efficiency optimization value of the battery energy storage system, the current battery charge state, the battery charging and discharging power, the current battery temperature, the current battery health degree, the power grid frequency deviation, the power grid voltage deviation, the power grid peak and valley load difference and the real-time power grid electricity price are quantified, and the efficiency optimization value of the battery energy storage system is generated. The problem of battery life loss or insufficient economical efficiency caused by a single optimization target in a traditional method is solved, the efficiency optimization value of the battery energy storage system not only reflects the characteristics of the battery energy storage system, but also serves the economical efficiency and stability of the overall dispatching target of a power grid, and therefore the operation intensity of the battery energy storage system is balanced, and the power grid performance is improved. The balance between the global efficiency and economical efficiency of the battery energy storage system is realized, so that the energy storage efficiency of the battery energy storage system is improved, and the service life of the battery in the battery energy storage system is prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing and monitoring, and in particular to a data processing system for monitoring power grid energy storage. Background Art

[0002] Grid energy storage includes battery energy storage systems (BESSs), which are devices used to store electrical energy and consist of battery packs, battery management systems, energy management systems, energy storage converters, and other electrical equipment. BESSs, with their fast response times and easy installation, have become a core supporting technology for grid peak and frequency regulation, as well as renewable energy consumption.

[0003] At present, the operation and control technology of battery energy storage systems still has the following problems: existing methods usually focus on a single optimization goal, aiming only at maximizing energy storage efficiency or minimizing operating costs, resulting in difficulty in balancing battery life and economy. They excessively pursue high-power charging and discharging, ignoring the influence of battery health and temperature, which can easily aggravate electrode aging and capacity decay, and significantly shorten battery cycle life. They simply rely on the difference between peak and valley electricity prices for charging and discharging scheduling, and respond lags in scenarios such as grid frequency fluctuations and voltage anomalies, making it impossible to effectively support grid stability. Summary of the Invention

[0004] In order to solve the technical problems existing in the background technology, the present invention proposes a data processing system for monitoring power grid energy storage.

[0005] The present invention proposes a data processing system for monitoring power grid energy storage, comprising a battery energy storage system, a data acquisition module, a data processing module and a control module;

[0006] Battery energy storage system: used to store electrical energy; battery energy storage system is a device used to store electrical energy in the prior art;

[0007] Data acquisition module: used to collect real-time grid load data, battery energy storage system operating status data and real-time grid electricity prices;

[0008] The operating status data of the battery energy storage system includes:

[0009] Current battery state of charge, battery charge and discharge power, current battery temperature, and current battery health;

[0010] The load data of the power grid includes:

[0011] Grid frequency deviation, grid voltage deviation, grid peak-valley load difference;

[0012] Data processing module: used to filter and normalize the real-time grid load data, battery energy storage system operating status data, and real-time grid electricity price collected in the data acquisition module to generate a feature set;

[0013] Control module: Generates the optimized efficiency value of the battery energy storage system based on the feature set obtained by the data processing module;

[0014] Based on the efficiency optimization value and constraints of the battery energy storage system, a genetic algorithm or particle swarm algorithm is used to generate the charging and discharging strategy of the battery energy storage system;

[0015] The generated charge and discharge strategy is sent to the controller of the battery energy storage system, and the controller controls the battery energy storage system to perform charge and discharge operations according to the generated charge and discharge strategy.

[0016] Preferably, in the control module, the battery energy storage system efficiency optimization value e b The calculation formula is:

[0017]

[0018] Where λ is the dynamic weight coefficient;

[0019] s is the current battery state of charge;

[0020] s max is the maximum state of charge of the battery;

[0021] α is the power influence coefficient, which is used to measure the impact of charging and discharging power on efficiency;

[0022] p is the absolute value of the battery charge and discharge power. The positive value of the battery charge and discharge power is charging, and the negative value is discharging;

[0023] β is the temperature influence coefficient;

[0024] T is the current battery temperature;

[0025] T opt The optimal operating temperature for the battery;

[0026] γ is the health impact coefficient;

[0027] h is the current health of the battery;

[0028] f grid is the grid demand function;

[0029] The values ​​of λ, α, β and γ are generated by genetic algorithm or particle swarm algorithm.

[0030] Preferably, in the control module, f grid The calculation formula is:

[0031]

[0032] Where, f dis the grid frequency deviation, obtained by subtracting the current frequency from the rated frequency;

[0033] V d is the grid voltage deviation, obtained by subtracting the current voltage from the rated voltage;

[0034] C g is the real-time grid electricity price;

[0035] C b is the base electricity price;

[0036] L p is the peak-valley load difference of the power grid, which is obtained by subtracting the maximum load from the minimum load in the current period;

[0037] k1, k2, k3 and k4 are all weight coefficients, and the values ​​of k1, k2, k3 and k4 are generated by genetic algorithm or particle swarm algorithm.

[0038] Preferably, in the control module, the constraints are as follows:

[0039] The current battery state of charge (SOC) is greater than or equal to the minimum battery state of charge (SOC), and the battery state of charge (SOC) is less than or equal to the maximum battery state of charge (SOC). max ;

[0040] The battery charge and discharge power p is greater than or equal to the battery's preset maximum discharge power, and the battery charge and discharge power p is less than or equal to the battery's preset maximum charge power;

[0041] Let P(t) and P(t+1) represent the battery charge and discharge power at time t and time t+1 respectively;

[0042] -ΔP max ≤P(t+1)-P(t)≤ΔP max , where ΔP max is the preset maximum charge and discharge power change rate;

[0043] The current battery temperature T is greater than or equal to the battery's preset minimum temperature, and the current battery temperature T is less than or equal to the battery's preset maximum temperature;

[0044] The current battery health h is greater than or equal to the battery's preset minimum health state, and the current battery health h is less than or equal to 1;

[0045] Let P grid (t) is the exchange power between the grid and the battery energy storage system at time t, P load (t) is the load power of the power grid at time t:

[0046] P grid (t)+P(t)=P load (t).

[0047] Preferably, it also includes a storage module;

[0048] The storage module is used to store the feature set in the data processing module and the charge and discharge strategy in the control module.

[0049] Preferably, a primary and backup dual database architecture is adopted in the storage module. The primary database and the backup database ensure data consistency through real-time data comparison and incremental synchronization mechanism. When the primary database is abnormal, the backup database takes over data storage.

[0050] Preferably, the data acquisition module includes a distributed sensor network, and performs data preprocessing on the real-time collected power grid load data, the operating status data of the battery energy storage system and the real-time power grid electricity price through the edge computing node.

[0051] A monitoring method for power grid energy storage data processing comprises the following steps:

[0052] S1, real-time collection of grid load data, battery energy storage system operating status data and real-time grid electricity prices;

[0053] The operating status data of the battery energy storage system includes:

[0054] Current battery state of charge, battery charge and discharge power, current battery temperature, and current battery health;

[0055] The load data of the power grid includes:

[0056] Grid frequency deviation, grid voltage deviation, grid peak-valley load difference;

[0057] S2: Filter and normalize the real-time grid load data, battery energy storage system operating status data, and real-time grid electricity prices collected in S1 to generate a feature set;

[0058] S3. Generate an optimized efficiency value of the battery energy storage system based on the feature set obtained in S2;

[0059] Based on the efficiency optimization value and constraints of the battery energy storage system, a genetic algorithm or particle swarm algorithm is used to generate the charging and discharging strategy of the battery energy storage system;

[0060] The generated charge and discharge strategy is sent to the controller of the battery energy storage system, and the controller controls the battery energy storage system to perform charge and discharge operations according to the generated charge and discharge strategy.

[0061] The present invention provides a data processing system for monitoring power grid energy storage, which has the following beneficial technical effects:

[0062] 1. In the process of generating the optimized efficiency value of the battery energy storage system, by quantifying the current battery state of charge, battery charge and discharge power, current battery temperature, current battery health, grid frequency deviation, grid voltage deviation, grid peak-valley load difference, and real-time grid electricity price, this solves the problem of battery life loss or insufficient economic efficiency caused by a single optimization objective in traditional methods. The optimized efficiency value of the battery energy storage system not only reflects the characteristics of the battery energy storage system itself, but also serves the economic and stability of the overall grid scheduling goals through the quantification of grid frequency deviation, grid voltage deviation, grid peak-valley load difference, and real-time grid electricity price, thereby balancing the operating intensity of the battery energy storage system and achieving a balance between the global efficiency and economy of the battery energy storage system, thereby improving the energy storage efficiency of the battery energy storage system and extending the battery life in the battery energy storage system.

[0063] 2. This application is based on the efficiency optimization value and constraints of the battery energy storage system, with the goal of maximizing the efficiency optimization value of the battery energy storage system. It uses a genetic algorithm or a particle swarm algorithm to generate a charging and discharging strategy for the battery energy storage system. By generating the efficiency optimization value of the battery energy storage system in real time and dynamically generating the charging and discharging strategy of the battery energy storage system, it can quickly respond to changes in the power grid and improve the stability and reliability of the power grid.

[0064] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a principle block diagram of the system of the present invention;

[0066] Figure 2 Flowchart of the method of the present invention. DETAILED DESCRIPTION

[0067] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar symbols throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention, and are not to be construed as limiting the present invention.

[0068] like Figure 1 A data processing system for monitoring power grid energy storage shown includes a battery energy storage system, a data acquisition module, a data processing module and a control module;

[0069] Battery energy storage system: used to store electrical energy; battery energy storage system is a device used to store electrical energy in the prior art;

[0070] Data acquisition module: used to collect real-time grid load data, battery energy storage system operating status data and real-time grid electricity prices;

[0071] The operating status data of the battery energy storage system includes:

[0072] Current battery state of charge, battery charge and discharge power, current battery temperature, and current battery health;

[0073] The load data of the power grid includes:

[0074] Grid frequency deviation, grid voltage deviation, grid peak-valley load difference;

[0075] Data processing module: used to filter and normalize the real-time grid load data, battery energy storage system operating status data, and real-time grid electricity price collected in the data acquisition module to generate a feature set;

[0076] In an optional embodiment, the data acquisition module includes a distributed sensor network and performs data preprocessing on real-time collected grid load data, battery energy storage system operating status data, and real-time grid electricity prices through edge computing nodes;

[0077] Control module: Generates the optimized efficiency value of the battery energy storage system based on the feature set obtained by the data processing module;

[0078] Based on the efficiency optimization value and constraints of the battery energy storage system, with the goal of maximizing the efficiency optimization value of the battery energy storage system, a genetic algorithm or a particle swarm algorithm is used to generate a charging and discharging strategy for the battery energy storage system;

[0079] The generated charge and discharge strategy is sent to the controller of the battery energy storage system, and the controller controls the battery energy storage system to perform charge and discharge operations according to the generated charge and discharge strategy;

[0080] Also included is a storage module;

[0081] The storage module is used to store the feature set in the data processing module and the charge and discharge strategy in the control module.

[0082] In an optional embodiment, a primary-backup dual database architecture is adopted in the storage module. The primary database and the backup database ensure data consistency through real-time data comparison and incremental synchronization mechanism. When the primary database is abnormal, the backup database takes over data storage.

[0083] In an optional embodiment, the battery energy storage system efficiency optimization value e b The calculation formula is:

[0084]

[0085] e b The bigger the better, bA larger value indicates higher energy conversion efficiency, lower losses, and better working conditions;

[0086] Where λ is the dynamic weight coefficient;

[0087] s is the current battery state of charge;

[0088] s max is the maximum state of charge of the battery;

[0089] α is the power influence coefficient, which is used to measure the impact of charging and discharging power on efficiency;

[0090] p is the absolute value of the battery charge and discharge power. The positive value of the battery charge and discharge power is charging, and the negative value is discharging;

[0091] β is the temperature influence coefficient;

[0092] T is the current battery temperature;

[0093] T opt The optimal operating temperature for the battery;

[0094] γ is the health impact coefficient;

[0095] h is the current health of the battery;

[0096] f grid is the grid demand function;

[0097] The values ​​of λ, α, β, and γ are generated by genetic algorithm or particle swarm algorithm;

[0098] In the formula, λ is used to reflect the priority adjustment of battery efficiency optimization based on the real-time demand of the power grid. When the power grid load fluctuates, the scheduling weight of the battery in the energy storage system is dynamically allocated by adaptively adjusting λ.

[0099] pass Characterizes the impact of the battery's current available capacity on efficiency. The closer the battery's state of charge s is to the battery's maximum state of charge s max , its charging and discharging efficiency is more likely to be limited by physical limits, such as overcharging or over-discharging loss; Make b It decreases linearly with the available capacity;

[0100] The loss of efficiency due to charge and discharge power is quantified by (1-α·p). The higher p is, the more significant the internal resistance loss and polarization effect of the battery are, and the more obvious the efficiency drop is.

[0101] By (1-β·|TT opt |) reflects the impact of temperature deviation from the optimal operating range on efficiency. The battery efficiency is at the optimal operating temperature T optWhen the current battery temperature Τ is the highest, overheating or overcooling will cause the electrolyte activity to decrease and the internal resistance to increase. opt | and temperature influence coefficient β, simulated e b Linear decreasing relationship with temperature deviation;

[0102] The long-term impact of battery aging on efficiency is reflected by (1-γ·(1-h)). The lower the current health of the battery h, the more serious the attenuation of the internal battery materials, such as electrode aging and capacity attenuation, which leads to a decrease in charge and discharge efficiency. The degree of aging is quantified by (1-h), combined with the health impact coefficient γ, so that e b It decreases linearly with the decline of health;

[0103] f grid The calculation formula is:

[0104]

[0105] Where, f d is the grid frequency deviation, obtained by subtracting the current frequency from the rated frequency;

[0106] V d is the grid voltage deviation, obtained by subtracting the current voltage from the rated voltage;

[0107] C g is the real-time grid electricity price;

[0108] C b is the base electricity price;

[0109] L p is the peak-valley load difference of the power grid, which is obtained by subtracting the maximum load from the minimum load in the current period;

[0110] k1, k2, k3 and k4 are all weight coefficients, and the values ​​of k1, k2, k3 and k4 are generated by genetic algorithm or particle swarm algorithm;

[0111] In the formula, the frequency deviation penalty term (1-k1·|f d |), when the frequency deviates from the rated value, the penalty term is reduced, forcing the energy storage system to respond to frequency regulation first;

[0112] By the voltage deviation penalty term (1-k2·|V d |), when the voltage fluctuates, the voltage is maintained stable through reactive / active power regulation of the energy storage system, such as rapid battery charging and discharging;

[0113] Gain term through electricity price utilization When the real-time electricity price is higher than the benchmark electricity price, the energy storage system is encouraged to discharge to make a profit; when it is lower than the benchmark electricity price, charging is encouraged to reduce the cost of purchasing electricity;

[0114] By smoothing the penalty term (1-k4·L p ), the greater the load peak-valley difference, the lower the penalty term, guiding the energy storage system to increase the intensity of peak shaving and valley filling;

[0115] Realize on-demand optimized multi-energy collaborative scheduling.

[0116] Efficiency optimization of a single energy storage system b It is easy to cause global suboptimality, such as overcharging and discharging of batteries to shorten their lifespan. By introducing f grid Implement grid demand constraints to balance the operating intensity of the battery energy storage system, achieve a balance between the overall efficiency and economy of the battery energy storage system, and enhance the full life cycle value of the battery energy storage system;

[0117] In the control module, unified quantitative indicators are provided for the coordinated control of the battery energy storage system by quantifying the current battery state of charge, battery charge and discharge power, current battery temperature, current battery health, grid frequency deviation, grid voltage deviation, grid peak and valley load difference, and real-time grid electricity price.

[0118] The optimized efficiency value of the battery energy storage system not only reflects its own characteristics, but also serves the economy and stability of the overall grid dispatch goals;

[0119] Ensure b It can not only represent the actual efficiency level of the battery under the current working conditions, but also be compared with the grid demand item f grid Form a synergy and ultimately support the generation of a global optimized scheduling strategy.

[0120] In an optional embodiment, in the control module, the constraints are as follows:

[0121] The current battery state of charge (SOC) is greater than or equal to the minimum battery state of charge (SOC), and the battery state of charge (SOC) is less than or equal to the maximum battery state of charge (SOC). max ;

[0122] The battery charge and discharge power p is greater than or equal to the battery's preset maximum discharge power, and the battery charge and discharge power p is less than or equal to the battery's preset maximum charge power;

[0123] Let P(t) and P(t+1) represent the battery charge and discharge power at time t and time t+1 respectively;

[0124] -ΔP max ≤P(t+1)-P(t)≤ΔP max , where ΔP max is the preset maximum charge and discharge power change rate;

[0125] The current battery temperature T is greater than or equal to the battery's preset minimum temperature, and the current battery temperature T is less than or equal to the battery's preset maximum temperature;

[0126] The current battery health h is greater than or equal to the battery's preset minimum health state, and the current battery health h is less than or equal to 1;

[0127] Let P grid (t) is the exchange power between the grid and the battery energy storage system at time t, P load (t) is the load power of the power grid at time t:

[0128] P grid (t)+P(t)=P load (t);

[0129] In an optional embodiment, 20%≤SOC≤90%;

[0130] In an optional embodiment, the data transmission process of the present application adopts 5G and power carrier dual-channel transmission;

[0131] By generating the optimal efficiency value of the battery energy storage system in real time, the optimal charging and discharging strategy of the battery energy storage system is dynamically generated. For example, when electricity prices are low, if the battery is in good condition and has a large charging space, the system will prioritize controlling the battery to charge at an appropriate power; during peak hours, the battery discharge is controlled to meet the grid load demand, thereby achieving effective peak-to-valley shifting and improving the stability and reliability of the grid.

[0132] Generating the battery energy storage system's efficiency optimization value in real time enables the system to quickly respond to changes in the power grid. When a sudden load fluctuation or fault occurs on the power grid, the system can quickly adjust the battery's charge and discharge status based on the current efficiency optimization value, providing emergency power support to the grid and enhancing the grid's anti-interference capabilities.

[0133] By factoring in grid electricity prices and calculating the optimal energy storage efficiency in real time, the system can charge when electricity prices are low and discharge when prices are high, thereby reducing the grid's electricity purchase costs. At the same time, the impact of battery health is taken into account to avoid damage to the battery caused by excessive charging and discharging, extending the battery life and reducing battery replacement costs.

[0134] The generation of the battery energy storage system efficiency optimization value includes battery temperature and charge and discharge power. By calculating and generating the battery energy storage system efficiency optimization value in real time, the system can monitor the battery's operating status in real time. When the battery temperature is too high or the charge and discharge power is too high, the system will automatically adjust the charge and discharge strategy to avoid dangerous situations such as overheating, overcharging, and over-discharging, ensuring the safe operation of the battery.

[0135] The generation of optimized efficiency values ​​for battery energy storage systems takes battery health into account, guiding the system to adopt more rational charging and discharging methods and reducing battery aging. For example, high-power charging and discharging operations can be avoided when battery health is low, thereby extending battery life and reducing the overall cost of the energy storage system.

[0136] like Figure 2 A monitoring method for power grid energy storage data processing shown includes the following steps:

[0137] S1, real-time collection of grid load data, battery energy storage system operating status data and real-time grid electricity prices;

[0138] The operating status data of the battery energy storage system includes:

[0139] Current battery state of charge, battery charge and discharge power, current battery temperature, and current battery health;

[0140] The load data of the power grid includes:

[0141] Grid frequency deviation, grid voltage deviation, grid peak-valley load difference;

[0142] S2: Filter and normalize the real-time grid load data, battery energy storage system operating status data, and real-time grid electricity prices collected in S1 to generate a feature set;

[0143] S3. Generate an optimized efficiency value of the battery energy storage system based on the feature set obtained in S2;

[0144] Based on the efficiency optimization value and constraints of the battery energy storage system, a genetic algorithm or particle swarm algorithm is used to generate the charging and discharging strategy of the battery energy storage system;

[0145] The generated charge and discharge strategy is sent to the controller of the battery energy storage system, and the controller controls the battery energy storage system to perform charge and discharge operations according to the generated charge and discharge strategy.

[0146] Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0147] In the embodiments provided by the present invention, it should be understood that the disclosed systems or methods can be implemented in other ways. For example, the embodiments of the invention described above are merely illustrative. For example, the division of modules is only a logical function division, and other division methods may be used in actual implementation.

[0148] Modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, and may be located in one place or distributed across multiple network modules. Some or all of these modules may be selected to achieve the objectives of this embodiment based on actual needs.

[0149] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing module, each module may exist physically separately, or two or more modules may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or hardware plus software functional modules.

[0150] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the basic characteristics of the present invention.

[0151] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A data processing system for monitoring power grid energy storage, characterized in that: Including battery energy storage system, data acquisition module, data processing module and control module; Battery energy storage system: used to store electrical energy; battery energy storage system is a device used to store electrical energy in the prior art; Data acquisition module: used to collect real-time grid load data, battery energy storage system operating status data and real-time grid electricity prices; The operating status data of the battery energy storage system includes: Current battery state of charge, battery charge and discharge power, current battery temperature, and current battery health; The load data of the power grid includes: Grid frequency deviation, grid voltage deviation, grid peak-valley load difference; Data processing module: used to filter and normalize the real-time grid load data, battery energy storage system operating status data, and real-time grid electricity price collected in the data acquisition module to generate a feature set; Control module: Generates the optimized efficiency value of the battery energy storage system based on the feature set obtained by the data processing module; Based on the efficiency optimization value and constraints of the battery energy storage system, a genetic algorithm or particle swarm algorithm is used to generate the charging and discharging strategy of the battery energy storage system; The generated charge and discharge strategy is sent to the controller of the battery energy storage system, and the controller controls the battery energy storage system to perform charge and discharge operations according to the generated charge and discharge strategy.

2. The data processing system for monitoring power grid energy storage according to claim 1, characterized in that: In the control module, the battery energy storage system efficiency optimization value e b The calculation formula is: Where λ is the dynamic weight coefficient; s is the current battery state of charge; s max is the maximum state of charge of the battery; α is the power influence coefficient, which is used to measure the impact of charging and discharging power on efficiency; p is the absolute value of the battery charge and discharge power. The positive value of the battery charge and discharge power is charging, and the negative value is discharging; β is the temperature influence coefficient; T is the current battery temperature; T opt The optimal operating temperature for the battery; γ is the health impact coefficient; h is the current health of the battery; f grid is the grid demand function; The values ​​of λ, α, β and γ are generated by genetic algorithm or particle swarm algorithm.

3. The data processing system for monitoring power grid energy storage according to claim 2, characterized in that: In the control module, f grid The calculation formula is: Where, f d is the grid frequency deviation, obtained by subtracting the current frequency from the rated frequency; V d is the grid voltage deviation, obtained by subtracting the current voltage from the rated voltage; C g is the real-time grid electricity price; C b is the benchmark electricity price; L p is the peak-valley load difference of the power grid, which is obtained by subtracting the maximum load from the minimum load in the current period; k1, k2, k3 and k4 are all weight coefficients, and the values ​​of k1, k2, k3 and k4 are generated by genetic algorithm or particle swarm algorithm.

4. The data processing system for monitoring power grid energy storage according to claim 3, characterized in that: In the control module, the constraints are as follows: The current battery state of charge (SOC) is greater than or equal to the minimum battery state of charge (SOC), and the battery state of charge (SOC) is less than or equal to the maximum battery state of charge (SOC). max ; The battery charge and discharge power p is greater than or equal to the battery's preset maximum discharge power, and the battery charge and discharge power p is less than or equal to the battery's preset maximum charge power; Let P(t) and P(t+1) represent the battery charge and discharge power at time t and time t+1 respectively; -ΔP max ≤P(t+1)-P(t)≤ΔP max , where ΔP max is the preset maximum charge and discharge power change rate; The current battery temperature T is greater than or equal to the battery's preset minimum temperature, and the current battery temperature T is less than or equal to the battery's preset maximum temperature; The current battery health h is greater than or equal to the battery's preset minimum health state, and the current battery health h is less than or equal to 1; Let P grid (t) is the exchange power between the grid and the battery energy storage system at time t, P laod (t) is the load power of the power grid at time t: P grid (t)+P(t)=P load (t)。 5. The data processing system for monitoring power grid energy storage according to claim 1, characterized in that: Also included is a storage module; The storage module is used to store the feature set in the data processing module and the charge and discharge strategy in the control module.

6. The data processing system for monitoring power grid energy storage according to claim 5, characterized in that: In the storage module, a master-slave dual database architecture is adopted. The master database and the standby database ensure data consistency through real-time data comparison and incremental synchronization mechanism. When the master database is abnormal, the standby database takes over data storage.

7. The data processing system for monitoring power grid energy storage according to claim 1, characterized in that: The data acquisition module includes a distributed sensor network and performs data preprocessing on the real-time collected grid load data, battery energy storage system operating status data and real-time grid electricity prices through edge computing nodes.

8. The monitoring method for power grid energy storage data processing according to any one of claims 1 to 7, characterized in that: The following steps are involved: S1, real-time collection of grid load data, battery energy storage system operating status data and real-time grid electricity prices; The operating status data of the battery energy storage system includes: Current battery state of charge, battery charge and discharge power, current battery temperature, and current battery health; The load data of the power grid includes: Grid frequency deviation, grid voltage deviation, grid peak-valley load difference; S2: Filter and normalize the real-time grid load data, battery energy storage system operating status data, and real-time grid electricity prices collected in S1 to generate a feature set; S3. Generate an optimized efficiency value of the battery energy storage system based on the feature set obtained in S2; Based on the efficiency optimization value and constraints of the battery energy storage system, a genetic algorithm or particle swarm algorithm is used to generate the charging and discharging strategy of the battery energy storage system; The generated charge and discharge strategy is sent to the controller of the battery energy storage system, and the controller controls the battery energy storage system to perform charge and discharge operations according to the generated charge and discharge strategy.

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