Intelligent management and control system and method for water-based sodium-ion battery energy storage power station

Abstract

The invention discloses an intelligent management and control system and method for a water-based sodium-ion battery energy storage power station, and relates to the technical field of new energy, and the system comprises a data collection and monitoring module, a fault diagnosis and early warning module, an automatic power generation control module, an energy management system, a safety management module, a maintenance and operation management module, and a user interface and report module. According to the intelligent management and control system for the water-based sodium-ion battery energy storage power station, by introducing an advanced machine learning algorithm and enhanced data processing capability, the intelligent management and control system for the water-based sodium-ion battery energy storage power station can realize more accurate real-time monitoring and faster response capability, so that the operation efficiency and safety of the power station are remarkably improved; operation and maintenance cost is reduced, and stability and reliability of the power grid are enhanced. Besides, the optimal management of the system can deal with market changes more effectively, the economic benefits and environmental sustainability of the power station are improved, and important support is provided for energy transformation.

Description

Technical Field

[0001] The present invention relates to the field of new energy technologies, and specifically to an intelligent management and control system and method for an aqueous sodium ion battery energy storage power station. Background Art

[0002] Aqueous sodium-ion batteries are an emerging energy storage technology that has gradually become the focus of research and commercial applications due to their low cost, high safety and environmental protection characteristics. This type of battery uses aqueous solution as electrolyte and utilizes the movement of sodium ions between positive and negative electrodes to store and release energy. Compared with traditional lithium-ion batteries, aqueous sodium-ion batteries have the advantages of low cost, abundant raw materials and low environmental impact.

[0003] However, aqueous sodium-ion batteries also have some technical challenges, especially limitations in energy density, cycle life and temperature sensitivity. These technical limitations affect the widespread application of batteries, especially in energy storage systems that require high energy output and long-term stable operation.

[0004] The current intelligent management and control system of water-based sodium-ion battery energy storage power stations still has deficiencies in real-time data processing, fault prevention, and energy optimization management. For example, the data processing speed of the existing system may not meet the needs of large-scale power stations, resulting in data delays or inaccuracies. In addition, the fault diagnosis and early warning functions are not mature enough to effectively predict and prevent all potential faults. The response speed and optimization capabilities of the energy management system in a dynamic market environment also need to be improved. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides an intelligent management and control system and method for an aqueous sodium ion battery energy storage power station to solve the problems raised in the above background technology.

[0006] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: an intelligent management and control system for a water-based sodium-ion battery energy storage power station, comprising a data acquisition and monitoring module, a fault diagnosis and early warning module, an automatic power generation control module, an energy management system, a safety management module, a maintenance and operation management module, and a user interface and reporting module;

[0007] The input end of the data acquisition and monitoring module receives various data from voltage, current, temperature, humidity sensors, inverters, and cooling system equipment in the power station;

[0008] The output end of the data acquisition and monitoring module transmits the pre-processed data to the fault diagnosis and early warning module; at the same time, the processed data is also transmitted to the energy management system for energy optimization analysis;

[0009] The input end of the fault diagnosis and early warning module receives real-time and historical data provided by the data acquisition and monitoring module;

[0010] The output end of the fault diagnosis and warning module outputs fault and warning information to the operator and the maintenance and operation management module for taking maintenance and repair measures; the diagnosis results are also notified to the user interface and reporting module for generating relevant reports and logs;

[0011] The input end of the automatic power generation control module receives power station status and power grid demand information from the data acquisition and monitoring module; and receives charging and discharging strategies and operating instructions from the energy management system;

[0012] The output terminal of the automatic power generation control module outputs power generation instructions to directly control the inverter and the battery management system to adjust the charging and discharging behavior of the battery;

[0013] The input end of the energy management system receives real-time data from the data acquisition and monitoring module and equipment status information from the fault diagnosis and early warning module;

[0014] The output of the energy management system sends the optimal charging and discharging strategy to the automatic generation control module; and transmits the energy optimization strategy and operation adjustment instructions to the user interface and reporting module for operator monitoring and adjustment;

[0015] The input end of the safety management module receives real-time monitoring data from the data acquisition and monitoring module, and preferentially receives information about battery status and environmental conditions;

[0016] The output terminal of the safety management module outputs safety threshold alarms and emergency measures, which are transmitted to the automatic power generation control module and the user interface and reporting module;

[0017] The input end of the maintenance and operation management module receives maintenance requirements and equipment status updates from the fault diagnosis and early warning module, and obtains equipment operation data from the data acquisition and monitoring module;

[0018] The output end of the maintenance and operation management module outputs the maintenance plan and operation report to the user interface and reporting module for recording and further decision-making;

[0019] The input end of the user interface and reporting module receives data, alarm information, operation status and maintenance records of all the above modules;

[0020] The output end of the user interface and reporting module provides an interactive interface between the operator and the system, and the operation commands are fed back to each module; and supports remote access function to ensure monitoring and control capabilities at any location.

[0021] To further optimize the technical solution, the data acquisition and monitoring module standardizes the data when preprocessing the data so that the range is between 0 and 1, and establishes a battery health assessment model;

[0022] The battery health assessment model is as follows:

[0023]

[0024] Among them, V i is at time t i The battery voltage, μ V is the expected value of the voltage, λ is the attenuation factor, which is used to reflect the attenuation rate of battery performance over time, and N is the number of sampling points.

[0025] To further optimize the technical solution, the data acquisition and monitoring module also has a built-in load prediction model, which is as follows:

[0026] [P load (t)=β0+β1·T(t)+β2·H(t)+∈(t)],

[0027] Among them, P load (t) is the predicted load power, T(t) and H(t) are the temperature and humidity at time t, β0, β1, β2 are model parameters, and ∈(t) is the error term;

[0028] The load prediction model uses historical data and real-time environmental parameters to predict future battery loads.

[0029] Further optimizing the technical solution, the fault diagnosis and early warning module is based on a machine learning algorithm, which is used to analyze the data obtained from the data acquisition and monitoring module and promptly identify potential faults of the battery system and related equipment;

[0030] The fault diagnosis and early warning module has a built-in fault detection model and a fault prediction model;

[0031] The fault detection model uses time series data:

[0032] (X=x1,x2,...,x t ) to assess the health of the device:

[0033] (H t )[H t =g(X;φ)],

[0034] Among them, X is the time series data of the equipment, reflecting the operating status of the equipment;

[0035] H tIt is the health status of the device, quantifying the current operating status of the device;

[0036] g is the state evaluation function, φ is the model parameter;

[0037] The fault prediction model predicts possible future faults based on historical data and current health status:

[0038] (F future )[F future =h(H t , t;ψ)]

[0039] Where h is the fault prediction function and ψ is the model parameter;

[0040] The fault diagnosis and early warning module ensures that the power station can respond to potential problems in real time, reduce downtime, and improve system reliability and efficiency.

[0041] To further optimize this technical solution, the automatic power generation control module establishes a data interface with the AGC and AVC systems of the power grid, exchanges control signals and status feedback in real time, processes data in real time and issues control commands, and establishes integrated control with AGC and AVC, and optimizes the operation of the power station by integrating AGC and AVC instructions. The optimized power station operation model is shown below:

[0042]

[0043] Where ΔP is the power to be adjusted, e(t) is the deviation of the frequency or voltage, and K p , K i , K d are the proportional, integral, and differential coefficients of the controller.

[0044] Further optimizing the technical solution, the energy management system uses a prediction model and an optimization algorithm to calculate the optimal charging and discharging time and amount to maximize profits and battery life, while automatically adjusting the charging and discharging plan according to changes in electricity prices and demand;

[0045] The energy management system has built-in charging / discharging strategy optimization model and cost and benefit optimization model.

[0046] To further optimize the technical solution, the charging / discharging strategy optimization model includes:

[0047] Assume that the total energy of the power station is E, and the charging (+) or discharging (-) decision at time t is P t ,but:

[0048] [E t+1 =E t +ηP t Δt],

[0049] Among them, η represents the charge and discharge efficiency, Δt is the time interval;

[0050] The cost and benefit optimization model includes:

[0051] Considering the electricity price c t and power station operating costs t , optimize the total revenue R, then:

[0052]

[0053] Where T is the total length of the optimization period.

[0054] To further optimize the technical solution, the safety management module is used to monitor the battery status, environmental monitoring and the execution of safety protection measures in real time; by analyzing the collected data, the system promptly identifies parameters that exceed the safety threshold and automatically executes the safety procedures of emergency shutdown and cooling system startup;

[0055] The security management module has a built-in threshold determination model to set security thresholds for various monitoring parameters. The security threshold of each monitoring parameter x is set as θ x ,but:

[0056] [Alert x ={1 if x≥θ x 0 otherwise],

[0057] When Alert x =1, the system sounds an alarm and triggers an emergency response;

[0058] The safety management module has a built-in environmental risk assessment model. Assuming that the environmental parameters E include temperature T, humidity H and gas concentration G, then:

[0059] [Risk E =w1·f(T)+w2·f(H)+w3·f(G)],

[0060] Among them, f(x) is the nonlinear conversion function of parameter x, w1, w2, w3 are weight coefficients, which are adjusted according to actual operating experience.

[0061] To further optimize the technical solution, the safety management module also has a built-in emergency response decision model, which integrates battery status and environmental risks to set a comprehensive risk index R:

[0062]

[0063] Among them, α and β adjust the relative importance of battery status and environmental risks.

[0064] Compared with the prior art, the present invention provides an intelligent management and control system for an aqueous sodium ion battery energy storage power station, which has the following beneficial effects:

[0065] The intelligent management and control system of the water-based sodium-ion battery energy storage power station can achieve more accurate real-time monitoring and faster response capabilities by introducing advanced machine learning algorithms and enhanced data processing capabilities. This will significantly improve the operating efficiency and safety of the power station, reduce operation and maintenance costs, and enhance the stability and reliability of the power grid. In addition, the optimized management of the system can more effectively respond to market changes, improve the economic benefits and environmental sustainability of the power station, and provide important support for energy transformation. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 This is a functional schematic diagram of a data acquisition and monitoring module in a smart management and control system of a water-based sodium-ion battery energy storage power station proposed by the present invention;

[0067] Figure 2 This is a functional schematic diagram of a fault diagnosis and early warning module in a smart management and control system of a water-based sodium-ion battery energy storage power station proposed by the present invention;

[0068] Figure 3 This is a functional schematic diagram of an automatic power generation control module in a smart management and control system of a water-based sodium-ion battery energy storage power station proposed by the present invention;

[0069] Figure 4 This is a functional schematic diagram of an energy management system in a smart management and control system of a water-based sodium-ion battery energy storage power station proposed by the present invention;

[0070] Figure 5 This is a functional schematic diagram of a safety management module in a smart management and control system for a water-based sodium-ion battery energy storage power station proposed by the present invention;

[0071] Figure 6 This is a functional schematic diagram of the maintenance and operation management module in the intelligent control system of the aqueous sodium-ion battery energy storage power station proposed by the present invention. DETAILED DESCRIPTION

[0072] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0073] Example:

[0074] An intelligent management and control system for a water-based sodium-ion battery energy storage power station includes a data acquisition and monitoring module, a fault diagnosis and early warning module, an automatic power generation control module, an energy management system, a safety management module, a maintenance and operation management module, and a user interface and reporting module.

[0075] like Figure 1 As shown, the input end of the data acquisition and monitoring module receives various data from the voltage, current, temperature, humidity sensors, inverters, and cooling system equipment in the power station.

[0076] The output end of the data acquisition and monitoring module transmits the pre-processed data to the fault diagnosis and early warning module, and at the same time transmits the processed data to the energy management system for energy optimization analysis.

[0077] Responsible for collecting the operating data of all key equipment in the power station (such as aqueous sodium-ion batteries, inverters, cooling systems, etc.). Through the installed sensors and measuring devices, data such as voltage, current, temperature, humidity, etc. can be collected in real time and transmitted to the central processing system. These sensors are distributed in key locations such as the battery management system (BMS) and energy management system (EMS) to ensure the accuracy and real-time nature of the data. The monitoring module also includes data preprocessing functions to filter and normalize the raw data to provide high-quality data input for subsequent analysis and decision-making.

[0078] Wherein, when the data acquisition and monitoring module pre-processes the data, it standardizes the data so that the range is between 0 and 1, and establishes a battery health assessment model;

[0079] The battery health assessment model is as follows:

[0080]

[0081] Among them, V i is at time t i The battery voltage, μ V is the expected value of the voltage, λ is the attenuation factor, which is used to reflect the attenuation rate of battery performance over time, and N is the number of sampling points.

[0082] In this embodiment, the data acquisition and monitoring module also has a built-in load prediction model, which is as follows:

[0083] [P load (t)=β0+β1·T(t)+β2·H(t)+∈(t)],

[0084] Among them, P load(t) is the predicted load power, T(t) and H(t) are the temperature and humidity at time t, β0, β1, β2 are model parameters, and ∈(t) is the error term;

[0085] Using historical data, linear regression technology is used to fit the relationship between temperature, humidity and load power, and the model coefficients β0, β1, β2 are determined. In the real-time system, the real-time collected temperature T(t) and humidity H(t) are input into the model to predict the load power P in the future time period. load (t), thereby helping the system optimize its operation strategy.

[0086] Battery energy scheduling: Load forecasting results can be used to predict the future energy demand of the battery and help the system adjust the battery charging and discharging plan in advance.

[0087] Power plant efficiency optimization: Load forecasting combined with efficiency optimization algorithms can help the system optimize resource allocation and reduce energy waste.

[0088] Safety monitoring and early warning: If it is predicted that the load will be too large in the future, the corresponding protection mechanism can be activated in advance to avoid battery overload and damage.

[0089] like Figure 2 As shown, the input end of the fault diagnosis and early warning module receives real-time and historical data provided by the data acquisition and monitoring module.

[0090] The output end of the fault diagnosis and warning module outputs fault and warning information to the operator and the maintenance and operation management module for taking maintenance and repair measures. The diagnosis results are also notified to the user interface and reporting module for generating relevant reports and logs.

[0091] Based on machine learning algorithms, this module can analyze the data obtained from the data acquisition and monitoring module to promptly identify potential faults in the battery system and related equipment. By establishing a health status model for the equipment, the system can predict the time and type of faults and implement early warning functions. Fault diagnosis relies on historical fault data and real-time performance data, combined with expert systems and pattern recognition technology to improve the accuracy and timeliness of diagnosis. In addition, the module can automatically send fault alarm information to the operator and make preliminary maintenance and repair suggestions.

[0092] The fault diagnosis and early warning module is based on a machine learning algorithm and is used to analyze the data obtained from the data acquisition and monitoring module to promptly identify potential faults of the battery system and related equipment;

[0093] The fault diagnosis and early warning module has a built-in fault detection model and a fault prediction model;

[0094] The fault detection model uses time series data:

[0095] (X=x1,x2,...,x t ) to assess the health of the device:

[0096] (H t )[H t =g(X;φ)],

[0097] Among them, g is the state evaluation function and φ is the model parameter.

[0098] (X) is the time series data of the equipment, reflecting the operating status of the equipment.

[0099] (H t ) is the health status of the device, which quantifies the current operating status of the device.

[0100] Health status (H t ) is a quantitative reflection of the overall condition of the equipment, while specific fault prediction requires analysis of the health status (H t ) is associated with certain specific performance degradations of the device.

[0101] In practical applications, by analyzing a large amount of historical data, we can find out the health status (H t ) and failures. t ) drops below a certain threshold or undergoes certain changes, the system may experience certain types of failures.

[0102] For example, the battery health status (H t ) gradually decreases over time, and when it falls below a certain threshold, faults such as capacity decay or internal short circuit may occur.

[0103] The fault prediction model predicts possible future faults based on historical data and current health status:

[0104] (F future )[F future =h(H t , t;ψ)]

[0105] Where h is the fault prediction function and ψ is the model parameter;

[0106] F future is a possible fault in the future, through the fault prediction model (h(H t , t; ψ)) prediction. The model output parameters include failure probability, failure time and failure type, based on the health status of the equipment and the pattern of historical failure data.

[0107] The output of the fault prediction model may include the following parameters:

[0108] Failure probability: indicates the probability of equipment failure in the future.

[0109] Failure Time: Predict the specific time when equipment may fail.

[0110] Fault Type: Predicts the specific type of fault, such as a short circuit, capacity degradation, or other form of electrical failure in the battery.

[0111] The F future After that, the model will further predict the specific failure type. This can be achieved by comparing the failure mode with the historical data. For example:

[0112] If the health status of the device (H t ) continues to decrease and matches the historical fault records within a specific threshold range, the system can predict the specific fault type.

[0113] For example, if the battery's internal resistance continues to increase and the voltage curve shows a sharp drop during discharge, the system may predict that the battery is about to fail due to an internal short circuit.

[0114] The fault diagnosis and early warning module ensures that the power station can respond to potential problems in real time, reduce downtime, and improve system reliability and efficiency.

[0115] like Figure 3 As shown, the input end of the automatic power generation control module receives power station status and grid demand information from the data acquisition and monitoring module, and receives charging and discharging strategies and operating instructions from the energy management system.

[0116] The output end of the automatic power generation control module outputs a power generation instruction to directly control the inverter and the battery management system to adjust the charging and discharging behavior of the battery.

[0117] This module directly affects the power generation efficiency and response speed of the power station. By real-time monitoring of grid demand and power station status, the charging and discharging process of the aqueous sodium-ion battery is intelligently controlled to meet the immediate needs of the grid. The control strategy includes load scheduling based on forecasts and strategic scheduling in response to changes in market electricity prices. The automatic control module is also integrated with the AGC (automatic generation control) and AVC (automatic voltage control) systems of the power grid to ensure the optimization of power station operations while maintaining grid stability and voltage compliance.

[0118] Among them, the automatic power generation control module establishes a data interface with the AGC and AVC systems of the power grid, exchanges control signals and status feedback in real time, processes data in real time and issues control commands, and establishes integrated control with AGC and AVC, and optimizes the operation of the power station by integrating AGC and AVC instructions. The optimized power station operation model is as follows:

[0119]

[0120] Where ΔP is the power to be adjusted, e(t) is the deviation of the frequency or voltage, and K p , K i , K d are the proportional, integral, and differential coefficients of the controller.

[0121] K p , K i , K d The three control parameters corresponding to the PID controller are used to adjust the power output to cope with the deviation of the power frequency or voltage.

[0122] e(t) represents the deviation between the actual voltage or frequency and the set value.

[0123] AGC is mainly used to control the grid frequency and maintain the power output of the generator set and the grid frequency balance. The frequency deviation e(t) represents the difference between the grid frequency and the set value (usually 50Hz or 60Hz). AGC eliminates the frequency deviation by adjusting the generated power (ΔP).

[0124] AVC is mainly used to control the voltage level of the power grid and ensure that the voltage is stable at the target value. The voltage deviation e(t) refers to the difference between the actual voltage and the set voltage. AVC corrects the voltage deviation by adjusting reactive power or transformer tap position.

[0125] like Figure 4 As shown, the input end of the energy management system receives real-time data from the data acquisition and monitoring module and equipment status information from the fault diagnosis and early warning module.

[0126] The output of the energy management system sends the optimal charging and discharging strategy to the automatic generation control module, and transmits the energy optimization strategy and operation adjustment instructions to the user interface and reporting module for operator monitoring and adjustment.

[0127] The Energy Management System (EMS) is the core of the entire power plant's intelligent management and control, responsible for optimizing the power plant's operating strategy and energy flow. By receiving data and information from each module, the EMS can calculate the optimal charging and discharging strategy in real time to improve energy efficiency and economic benefits. This module also includes demand response management, which automatically adjusts the power plant's operating status according to the grid's demand and electricity price changes to reduce operating costs and increase revenue. In addition, the EMS also interfaces with the market trading system to support power plants in participating in the ancillary service market and energy trading.

[0128] The energy management system uses a prediction model and optimization algorithm to calculate the optimal charging and discharging time and amount to maximize profits and battery life, while automatically adjusting the charging and discharging plan according to changes in electricity prices and demand;

[0129] The energy management system has built-in charging / discharging strategy optimization model and cost and benefit optimization model.

[0130] In this embodiment, the charging / discharging strategy optimization model includes:

[0131] Assume that the total energy of the power station is E, and the charging (+) or discharging (-) decision at time t is P t ,but:

[0132] [E t+1 =E t +ηP t Δt],

[0133] Among them, η represents the charge and discharge efficiency, Δt is the time interval;

[0134] The cost and benefit optimization model includes:

[0135] Considering the electricity price c t and power station operating costs t , optimize the total revenue R, then:

[0136]

[0137] Where T is the total length of the optimization period.

[0138] like Figure 5 As shown, the input end of the safety management module receives real-time monitoring data from the data acquisition and monitoring module, and preferentially receives information about battery status and environmental conditions.

[0139] The output end of the safety management module outputs safety threshold alarms and emergency measures, which are transmitted to the automatic power generation control module and the user interface and reporting module.

[0140] Safety is an important part of power station management, especially considering the chemical characteristics of aqueous sodium-ion batteries and possible safety risks. The safety management module includes real-time monitoring of battery status, environmental monitoring (such as temperature, humidity, gas leakage) and the implementation of safety protection measures. By analyzing the collected data, the system can promptly identify parameters that exceed safety thresholds and automatically execute safety procedures such as emergency shutdown and cooling system startup to prevent accidents.

[0141] The safety management module is used to monitor the battery status, environmental monitoring and the implementation of safety protection measures in real time; by analyzing the collected data, the system promptly identifies parameters that exceed the safety threshold and automatically executes the safety procedures of emergency shutdown and cooling system startup;

[0142] The security management module has a built-in threshold determination model to set security thresholds for various monitoring parameters. The security threshold of each monitoring parameter x is set as θ x ,but:

[0143] [Alert x ={1 if x≥θ x 0 otherwise],

[0144] When Alert x =1, the system sounds an alarm and triggers an emergency response;

[0145] The safety management module has a built-in environmental risk assessment model. Assuming that the environmental parameters E include temperature T, humidity H and gas concentration G, then:

[0146] [Risk E =w1·f(T)+w2·f(H)+w3·f(G)],

[0147] Among them, f(x) is the nonlinear conversion function of parameter x, w1, w2, w3 are weight coefficients, which are adjusted according to actual operating experience.

[0148] In this embodiment, the safety management module also has a built-in emergency response decision model, which combines the battery status and environmental risks to set a comprehensive risk index R:

[0149]

[0150] Among them, α and β adjust the relative importance of battery status and environmental risks.

[0151] like Figure 6 As shown, the input end of the maintenance and operation management module receives maintenance requirements and equipment status updates from the fault diagnosis and early warning module, and obtains equipment operation data from the data acquisition and monitoring module.

[0152] The output end of the maintenance and operation management module outputs the maintenance plan and operation report to the user interface and reporting module for recording and further decision-making.

[0153] This module ensures efficient operation and long-term reliability of the power plant. It includes asset management, maintenance planning, spare parts management and technician scheduling. By analyzing the operating data and health status of the equipment, the system can predict maintenance needs and the best maintenance time to achieve preventive maintenance. In addition, the module also provides automatic generation of operation logs, performance reports and maintenance records to support operational decisions and compliance reports.

[0154] The input end of the user interface and reporting module receives data, alarm information, operating status and maintenance records of all the above modules.

[0155] The output of the user interface and reporting module provides an interactive interface between the operator and the system, and the operation commands are fed back to each module. It also supports remote access to ensure monitoring and control capabilities at any location.

[0156] The user interface is the window for operators to interact with the system, providing an intuitive graphical interface to display real-time data, system status, alarm information and operation control options. The report module automatically generates power plant operation reports, event logs, performance analysis and maintenance records to help managers make decision support. In addition, the module also supports remote access to ensure that the operation status of the power plant can be monitored and controlled from any location.

[0157] The beneficial effects of the present invention are:

[0158] The intelligent management and control system of the water-based sodium-ion battery energy storage power station can achieve more accurate real-time monitoring and faster response capabilities by introducing advanced machine learning algorithms and enhanced data processing capabilities. This will significantly improve the operating efficiency and safety of the power station, reduce operation and maintenance costs, and enhance the stability and reliability of the power grid. In addition, the optimized management of the system can more effectively respond to market changes, improve the economic benefits and environmental sustainability of the power station, and provide important support for energy transformation.

[0159] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0160] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent management and control system for a water-based sodium-ion battery energy storage power station, characterized in that: It includes data acquisition and monitoring module, fault diagnosis and early warning module, automatic power generation control module, energy management system, safety management module, maintenance and operation management module, and user interface and reporting module; The input end of the data acquisition and monitoring module receives various data from voltage, current, temperature, humidity sensors, inverters, and cooling system equipment in the power station; The output end of the data acquisition and monitoring module transmits the pre-processed power station data to the fault diagnosis and early warning module; at the same time, the processed data is also transmitted to the energy management system for energy optimization analysis; The input end of the fault diagnosis and early warning module receives real-time and historical data provided by the data acquisition and monitoring module; The output end of the fault diagnosis and warning module outputs fault and warning information to the operator and the maintenance and operation management module for taking maintenance and repair measures; the diagnosis results are also notified to the user interface and reporting module for generating relevant reports and logs; The input end of the automatic power generation control module receives power station status and power grid demand information from the data acquisition and monitoring module; and receives charging and discharging strategies and operating instructions from the energy management system; The output terminal of the automatic power generation control module outputs power generation instructions to directly control the inverter and the battery management system to adjust the charging and discharging behavior of the battery; The input end of the energy management system receives real-time data from the data acquisition and monitoring module and equipment status information from the fault diagnosis and early warning module; The output end of the energy management system sends the optimal charging and discharging strategy to the automatic generation control module; and passing energy optimization strategies and operation adjustment instructions to the user interface and reporting module for operator monitoring and adjustment; The input end of the safety management module receives real-time monitoring data from the data acquisition and monitoring module, and preferentially receives information about battery status and environmental conditions; The output terminal of the safety management module outputs safety threshold alarms and emergency measures, which are transmitted to the automatic power generation control module and the user interface and reporting module; The input end of the maintenance and operation management module receives maintenance requirements and equipment status updates from the fault diagnosis and early warning module, and obtains equipment operation data from the data acquisition and monitoring module; The output end of the maintenance and operation management module outputs the maintenance plan and operation report to the user interface and reporting module for recording and further decision-making; The input end of the user interface and reporting module receives data, alarm information, operation status and maintenance records of all the above modules; The output end of the user interface and reporting module provides an interactive interface between the operator and the system, and the operation commands are fed back to each module; And support remote access function to ensure monitoring and control capabilities at any location.

2. According to claim 1, a water-based sodium-ion battery energy storage power station intelligent management and control system is characterized in that: When the data acquisition and monitoring module pre-processes the power station data, it standardizes the data so that the range is between 0 and 1, and establishes a battery health assessment model; The battery health assessment model is as follows: Among them, V i is the battery voltage at time ti, μ V is the expected value of the voltage, λ is the attenuation factor, which is used to reflect the attenuation rate of battery performance over time, and N is the number of sampling points.

3. The intelligent management and control system of a water-based sodium ion battery energy storage power station according to claim 2 is characterized in that: The data acquisition and monitoring module also has a built-in load prediction model, which is as follows: [P load (t)=β0+β1·T(t)+β2·H(t)+∈(t)], Among them, P load (t) is the predicted load power, T(t) and H(t) are the temperature and humidity at time t, β0, β1, β2 are model parameters, and ∈(t) is the error term; The load prediction model uses historical data and real-time environmental parameters to predict future battery loads.

4. The intelligent management and control system of a water-based sodium ion battery energy storage power station according to claim 1 is characterized in that: The fault diagnosis and early warning module is based on a machine learning algorithm and is used to analyze the data obtained from the data acquisition and monitoring module to promptly identify potential faults of the battery system and related equipment; The fault diagnosis and early warning module has a built-in fault detection model and a fault prediction model; The fault detection model uses time series data: (X=x1,x2,...,x t ) to assess the health of the device: (H t )[H t =g(X;φ)], Among them, X is the time series data of the equipment, reflecting the operating status of the equipment, g is the state evaluation function, φ is the model parameter; H t It is the health status of the device, quantifying the current operating status of the device; The fault prediction model predicts possible future faults based on historical data and current health status: (F future )[F future =h(H t ,t;ψ)], Where h is the fault prediction function and ψ is the model parameter; F future The fault diagnosis and early warning module ensures that the power plant can respond to potential problems in real time, reduce downtime, and improve system reliability and efficiency.

5. The intelligent management and control system of a water-based sodium ion battery energy storage power station according to claim 1 is characterized in that: The automatic power generation control module establishes a data interface with the AGC and AVC systems of the power grid, exchanges control signals and status feedback in real time, processes data in real time and issues control commands, and establishes integrated control with AGC and AVC, and optimizes the operation of the power station by integrating AGC and AVC instructions. The optimized power station operation model is shown below: Where ΔP is the power to be adjusted, e(t) is the deviation of the frequency or voltage, and K p , K i , K d are the proportional, integral, and differential coefficients of the controller.

6. The intelligent management and control system of a water-based sodium-ion battery energy storage power station according to claim 1 is characterized in that: The energy management system uses prediction models and optimization algorithms to calculate the optimal charging and discharging times and amounts to maximize profits and battery life, while automatically adjusting charging and discharging plans based on changes in electricity prices and demand; The energy management system has built-in charging / discharging strategy optimization model and cost and benefit optimization model.

7. The intelligent management and control system of a water-based sodium-ion battery energy storage power station according to claim 6 is characterized in that: The charging / discharging strategy optimization model includes: Assume that the total energy of the power station is E, and the charging (+) or discharging (-) decision at time t is P t ,but: [AND t+1 =And t +ηP t Δt], Among them, η represents the charge and discharge efficiency, Δt is the time interval; The cost and benefit optimization model includes: Considering the electricity price c t and power station operating costs t , optimize the total revenue R, then: Where T is the total length of the optimization period.

8. The intelligent management and control system of a water-based sodium-ion battery energy storage power station according to claim 1 is characterized in that: The safety management module is used to monitor the battery status, environmental monitoring and the implementation of safety protection measures in real time; by analyzing the collected data, the system promptly identifies parameters that exceed the safety threshold and automatically executes the safety procedures of emergency shutdown and cooling system startup; The security management module has a built-in threshold determination model to set security thresholds for various monitoring parameters. The security threshold of each monitoring parameter x is set as θ x ,but: [Alert x ={1 if x≥θ x 0 otherwise], When Alet x =1, the system sounds an alarm and triggers an emergency response; The safety management module has a built-in environmental risk assessment model. Assuming that the environmental parameters E include temperature Y, humidity h and gas concentration G, then: [Risk E =w1·f(T)+w2·f(H)+w3·f(G)], Among them, f(x) is the nonlinear conversion function of parameter x, w1, w2, w3 are weight coefficients, which are adjusted according to actual operating experience.

9. The intelligent management and control system of a water-based sodium-ion battery energy storage power station according to claim 8, characterized in that: The safety management module also has a built-in emergency response decision model, which combines battery status and environmental risks to set a comprehensive risk index R: Among them, α and β adjust the relative importance of battery status and environmental risks.

10. A method for intelligent management and control of an aqueous sodium ion battery energy storage power station, characterized in that: An implementation method of an intelligent management and control system for an aqueous sodium-ion battery energy storage power station based on any one of claims 1 to 9.

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