Electric energy supply system and method based on hydrogen ammonia energy storage

Abstract

The invention discloses an electric energy supply system and method based on hydrogen ammonia energy storage, and belongs to the technical field of clean energy power generation. In order to solve the problems of intermittency and volatility of renewable energy sources, energy waste, unbalanced supply and demand, and low energy storage and release efficiency of a traditional energy storage mode, the hydrogen-ammonia battery combines hydrogen and an ammonia water solution, stores and releases energy, can ensure continuous supply of energy, can store a large amount of hydrogen, and improves the energy storage efficiency. By integrating data monitoring, anomaly detection, energy management scheduling, state evaluation diagnosis and an intelligent control execution unit, intelligent management of the hydrogen-ammonia energy storage electric energy supply system is realized, operation faults can be found and processed in time, and electric power demand changes can be predicted and responded. Resource allocation is optimized, energy waste is reduced, and sudden change of power grid load is quickly adapted, so that the stability, the response speed and the energy utilization rate of the system are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of clean energy power generation technology, and in particular to an electric energy supply system and method based on hydrogen and ammonia energy storage. Background Art

[0002] As a secondary new energy source, hydrogen energy has the characteristics of wide sources, clean and environmentally friendly utilization process, high energy conversion efficiency and high energy density. The application of hydrogen energy is also very extensive and can be used in many industries such as chemical industry, smelting, manufacturing, electricity, transportation, etc. However, there are still the following problems in actual operation: Renewable energy sources such as solar and wind energy are intermittent and volatile, resulting in unstable power supply. At the same time, excess renewable energy generation often results in energy waste, and when electricity demand increases, there may be a problem of insufficient power supply. Traditional energy storage methods such as pumped storage and compressed air storage are inefficient and slow to respond. Existing energy storage and supply systems may lead to system instability or safety accidents due to equipment failure or abnormal operation. Summary of the invention

[0003] The purpose of the present invention is to provide an electric energy supply system and method based on hydrogen and ammonia energy storage to solve the problems raised in the above background technology.

[0004] To achieve the above object, the present invention provides the following technical solution: an electric energy supply system based on hydrogen-ammonia energy storage, comprising: Hydrogen generation unit for: Electrolyze water through an electrolyzer and extract hydrogen from the water, remove impurities from the extracted hydrogen through a purifier, and store or transport it to a hydrogen-ammonia battery to provide hydrogen supply; Ammonia generating unit, used for: The hydrogen and nitrogen are synthesized by a Haber-Bosch synthesis device to synthesize ammonia and the ammonia is dissolved in water to form an ammonia solution, which is stored in an ammonia solution storage tank for standby use; Electrical energy storage unit for: When renewable energy generation exceeds demand, the excess electricity is input into the lithium-ion battery energy storage device through the charge and discharge control system to store the excess electricity. When the demand for electricity increases, the charge and discharge control system releases the electricity from the lithium-ion battery energy storage device to supply the grid. Hydrogen-ammonia battery cells for: The hydrogen gas and ammonia solution are combined at the anode and cathode of the hydrogen-ammonia battery stack, and the electrons generated by the hydrogen-ammonia battery stack flow through the external circuit to form an electric current, driving the load to work or connecting to the grid for power generation; Intelligent control module for: Monitor the operating status of the hydrogen generation unit, ammonia generation unit, electric energy storage unit and hydrogen-ammonia battery unit, adjust the production and storage ratio of hydrogen and ammonia, and control the balance of electricity supply and demand.

[0005] Further, the hydrogen generation unit includes an electrolyzer, a DC power supply, a gas separation device, a purifier and a high-pressure gas tank; The DC power supply is connected to the electrolytic cell, which is connected to the gas separation device. The gas separation device is used to separate the gas generated by the electrolytic cell. The separated hydrogen enters the purifier. The purifier is used to purify the hydrogen. The purified hydrogen enters the high-pressure gas tank for storage or is transported to the ammonia generation unit through a pipeline.

[0006] Furthermore, the ammonia water generating unit includes a nitrogen and hydrogen supply system, a Haber-Bosch synthesis device, an ammonia water preparation system and an ammonia water solution storage tank; Among them, the nitrogen and hydrogen supply system is used to input nitrogen and hydrogen to the Haber-Bosch synthesis device. The hydrogen is connected to the Haber-Bosch synthesis device from the hydrogen generation unit through a pipeline. The nitrogen is separated from the air and then input into the Haber-Bosch synthesis device. The Haber-Bosch synthesis device is used to synthesize ammonia. The generated ammonia enters the ammonia water preparation system to react with water to generate an ammonia solution. The generated ammonia solution is stored in an ammonia solution storage tank.

[0007] Further, the electric energy storage unit includes a lithium-ion battery energy storage device and a charge and discharge control system; Wherein, the charge and discharge control system is connected to the lithium-ion battery energy storage device, and the charge and discharge control system is used to control the charge and discharge state of the lithium-ion battery energy storage device, and the lithium-ion battery energy storage device is connected to the power grid; The hydrogen-ammonia battery unit includes a hydrogen-ammonia battery stack including a plurality of membrane electrode assemblies, a fuel supply system, and an energy conversion system; Among them, the fuel supply system is used to transport hydrogen and ammonia solution to the anode and cathode of the hydrogen-ammonia battery stack respectively. The fuel supply system is provided with a hydrogen pipeline and an ammonia pipeline. The hydrogen pipeline is connected from the hydrogen generation unit to the anode side of the hydrogen-ammonia battery stack, and the ammonia pipeline is connected from the ammonia generation unit to the cathode side of the hydrogen-ammonia battery stack. The energy conversion system is used to output the electrical energy generated by the chemical reaction of the hydrogen-ammonia battery stack to the power grid or load.

[0008] Furthermore, the intelligent control module includes: Data acquisition and monitoring unit, used for: The operating status data of the hydrogen generation unit, ammonia generation unit, electric energy storage unit and hydrogen-ammonia battery unit are collected in real time through sensors arranged in each subsystem. The operating status data includes temperature, pressure, gas flow, liquid flow and electricity. The sensors include temperature sensors, pressure sensors, flow meters and electricity meters, which display various parameters in real time, monitor the load changes of the power grid and the power generation status of renewable energy, and store historical data records; Condition Assessment Diagnostic Unit for: Set the normal working range and threshold of the operating status parameters, evaluate the health status and performance of the hydrogen generation unit, ammonia generation unit, power storage unit and hydrogen-ammonia battery unit based on the collected operating status data, process the real-time data based on the anomaly detection algorithm, identify abnormal situations, and immediately trigger the alarm mechanism once an abnormality is detected, and notify the management personnel through the user interface and remote communication.

[0009] Furthermore, the data acquisition and monitoring unit includes: A real-time monitoring module is used to monitor in real time the data collection response time corresponding to each data collection of each sensor; A data acquisition characterization coefficient acquisition module is used to acquire a data acquisition characterization coefficient corresponding to each sensor according to a data acquisition response time corresponding to each sensor for each data acquisition; The data acquisition characterization coefficient is obtained by the following formula: ; Where F represents the data acquisition characterization coefficient; n represents the number of times the sensor collects data; T i represents the data collection response time corresponding to the i-th data collection; T e Indicates the maximum collection response time allowed for normal data collection operation; T z and T p Indicates the median and average values ​​of the data collection response time corresponding to n data collections; A first comparison module, used for comparing the data acquisition characterization coefficient corresponding to each sensor with a preset characterization coefficient threshold; An abnormal alarm module is used to issue a sensor data acquisition abnormal alarm when the data acquisition characterization coefficient corresponding to the sensor is lower than a preset characterization coefficient threshold; The sensor network quality detection module is used to perform operation quality detection on the operation of the entire sensor network using the data acquisition characterization coefficient corresponding to each sensor when the data acquisition characterization coefficients corresponding to the sensors are not lower than a preset characterization coefficient threshold.

[0010] Furthermore, the sensor network quality detection module includes: A data acquisition characterization coefficient retrieving module, used for retrieving the data acquisition characterization coefficient corresponding to each sensor when the data acquisition characterization coefficients corresponding to the sensors are not lower than a preset characterization coefficient threshold; A coefficient threshold retrieving module, used for retrieving a first coefficient threshold and a second coefficient threshold from a database; A second comparison module, configured to compare the data acquisition characterization coefficient with the first coefficient threshold and a second coefficient threshold; A first data acquisition characterization coefficient extraction module, used to extract the data acquisition characterization coefficient corresponding to the sensor exceeding the first coefficient threshold; A first comprehensive coefficient parameter acquisition module, used to acquire a first comprehensive coefficient parameter using the data acquisition characterization coefficient corresponding to the sensor exceeding the first coefficient threshold; The first comprehensive coefficient parameter is obtained by the following formula: ; Among them, S 01 represents the first comprehensive coefficient parameter; x represents the number of sensors whose data acquisition characterization coefficient exceeds the first coefficient threshold; F 01i represents the data acquisition characterization coefficient of the i-th sensor exceeding the first coefficient threshold; F y01 and F y02 represents the first coefficient threshold and the second coefficient threshold; F y Indicates the preset characterization coefficient threshold; S b01 represents the first coefficient fluctuation parameter, and the first coefficient fluctuation parameter is obtained by the following formula: ; Among them, S b represents the first coefficient fluctuation parameter; x represents the number of sensors whose data acquisition characterization coefficient exceeds the first coefficient threshold; F 01i represents the data acquisition characterization coefficient of the i-th sensor exceeding the first coefficient threshold; F y01 represents the first coefficient threshold; sqrt represents the square root function symbol; F b Indicates the standard deviation of the data acquisition characterization coefficients corresponding to all sensors; F y represents a preset characterization coefficient threshold; A second data acquisition characterization coefficient extraction module, used to extract data acquisition characterization coefficients corresponding to sensors that do not exceed the first coefficient threshold but exceed the second coefficient threshold; A second comprehensive coefficient parameter acquisition module, used to acquire a second comprehensive coefficient parameter using a data acquisition characterization coefficient corresponding to a sensor that does not exceed the first coefficient threshold but exceeds the second coefficient threshold; The second comprehensive coefficient parameter is obtained by the following formula: ; Among them, S 02 represents the second comprehensive coefficient parameter; y represents that the data acquisition characterization coefficient does not exceed the first coefficient threshold, but the number of sensors that exceeds the second coefficient threshold; F 02irepresents the data acquisition characterization coefficient of the i-th sensor that does not exceed the first coefficient threshold, but exceeds the second coefficient threshold; F b Indicates the standard deviation of the data acquisition characterization coefficients corresponding to all sensors; F p Represents the average value of the data acquisition characterization coefficients corresponding to all sensors; F y02 represents the second coefficient threshold; F 02b Indicates that the standard deviation of the data acquisition characterization coefficient of the sensor does not exceed the first coefficient threshold, but exceeds the second coefficient threshold; F 01b representing a standard deviation of a data collection characterization coefficient of a sensor exceeding a first coefficient threshold; The quality detection module is used to perform operation quality detection on the operation of the entire sensor network using the first comprehensive coefficient parameter and the second comprehensive coefficient parameter.

[0011] Furthermore, the operation process of the quality inspection module includes: Retrieve the first comprehensive coefficient parameter and the second comprehensive coefficient parameter; Retrieving a data acquisition characterization coefficient corresponding to a second coefficient threshold value that does not exceed the second coefficient threshold value; Obtaining a comprehensive coefficient parameter by using the first comprehensive coefficient parameter and the second comprehensive coefficient parameter in combination with a data acquisition characterization coefficient corresponding to a second coefficient threshold value that does not exceed the first comprehensive coefficient parameter; The comprehensive coefficient parameter is obtained by the following formula: ; Among them, S represents the comprehensive coefficient parameter; S 01 Represents the first comprehensive coefficient parameter; S 02 Represents the second comprehensive coefficient parameter; F 01max represents the maximum value of the data acquisition characterization coefficient of x sensors exceeding the first coefficient threshold; F 02max represents the maximum value of the data acquisition coefficient of y sensors that do not exceed the first coefficient threshold, but exceed the second coefficient threshold; F z Represents the median value of the data acquisition characterization coefficients corresponding to all sensors; F z Indicates that the middle value of the data acquisition characterization coefficient corresponding to the second coefficient threshold value is not exceeded; F 03b Indicates that the standard deviation of the data acquisition characterization coefficient corresponding to the second coefficient threshold is not exceeded; F 03max Indicates that the maximum value of the data acquisition characterization coefficient corresponding to the second coefficient threshold value is not exceeded; Comparing the comprehensive coefficient parameter with a preset comprehensive coefficient threshold; When the comprehensive coefficient parameter exceeds a preset comprehensive coefficient threshold, it is determined that the sensor network operation is abnormal, and an abnormal alarm is issued.

[0012] Furthermore, the intelligent control module further includes: Energy management and dispatching unit, used for: Formulate energy management strategies and set power demand priorities, where meeting immediate power demand takes precedence over energy storage and hydrogen / ammonia production. Forecast future power demand and renewable energy generation based on time series analysis, and dynamically adjust the operating status of each subsystem based on forecast results and real-time data; Among them, dynamically adjusting the operating status of each subsystem includes adjusting the power of the electrolyzer and the Haber-Bosch synthesis device, and controlling the charge and discharge rate of the lithium-ion battery; Intelligent control execution unit for: Receive instructions from the energy management dispatch unit and directly control the hardware equipment of the hydrogen generation unit, ammonia generation unit, electric energy storage unit and hydrogen-ammonia battery unit.

[0013] Furthermore, a method for supplying electric energy based on hydrogen-ammonia energy storage is applied to the above-mentioned electric energy supply system based on hydrogen-ammonia energy storage, comprising the following steps: System initialization monitoring, start the intelligent control module, initialize all sensors and actuators, and monitor the operating status of the hydrogen generation unit, ammonia generation unit, power storage unit, and hydrogen-ammonia battery unit in real time; Energy generation and distribution, using renewable energy to generate electricity, allocating power according to real-time power demand, and using excess electricity for the production of hydrogen and ammonia solutions; Hydrogen and ammonia production: electrolysis of water produces hydrogen, which is purified by a purifier and stored. The purified hydrogen is combined with nitrogen to form ammonia, which is then dissolved in water to form an ammonia solution for storage; Electric energy storage and release: when renewable energy generation exceeds demand, excess electric energy is stored in lithium-ion battery energy storage devices through a charge and discharge control system. When electricity demand increases, the electric energy in the lithium-ion battery energy storage devices is released to supply the grid. Hydrogen-ammonia battery power supply: When the lithium-ion battery energy storage device is short of power or the demand is at a peak, the hydrogen-ammonia battery unit is started to use the stored hydrogen and ammonia solution to react chemically in the hydrogen-ammonia battery stack to generate electricity, which is then output to the grid or load through the energy conversion system; Intelligent management optimization: the intelligent control module dynamically adjusts system operating parameters based on real-time data and historical records, and optimizes the balance of power supply and demand through the energy management and scheduling unit.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention generates hydrogen by electrolyzing water, and synthesizes hydrogen and nitrogen into ammonia. This process converts intermittent renewable energy into chemical energy that can be stored for a long time, avoiding the problem of unstable power supply caused by the intermittent and volatile nature of renewable energy. The hydrogen-ammonia battery unit directly converts the chemical energy in hydrogen and ammonia solution into electrical energy with high conversion efficiency, and can quickly release electrical energy when needed to meet the load demand of the power grid. The electrical energy storage unit in the system can store electrical energy when renewable energy generates excess power, and release electrical energy when electricity demand increases, effectively balancing the supply and demand relationship and reducing energy waste. The synergistic effect of the hydrogen generation unit, the ammonia generation unit and the electrical energy storage unit enables the system to flexibly adjust the operating status of each unit according to the real-time power demand and renewable energy power generation situation, thereby improving the overall energy utilization rate.

[0015] 2. The application of the intelligent control module of the present invention enables the system to monitor the operating status of each unit in real time, promptly detect and handle abnormal situations, and ensure the safe and stable operation of the system. Through data analysis and prediction, the intelligent control module can formulate the optimal energy management strategy to achieve optimal configuration and efficient utilization of energy. By integrating data monitoring, anomaly detection, energy management scheduling, status assessment diagnosis and intelligent control execution units, intelligent management of the hydrogen-ammonia energy storage power supply system is realized, which can promptly detect and handle operating faults, predict and respond to changes in electricity demand, optimize resource allocation, reduce energy waste, and quickly adapt to sudden changes in grid load, thereby significantly improving the stability, response speed and energy utilization of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the principle of the electric energy supply system based on hydrogen and ammonia energy storage of the present invention; Figure 2 This is a schematic flow chart of the method for supplying electric energy according to the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in 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.

[0018] In order to solve the problem of intermittent and volatile renewable energy such as solar and wind energy, which leads to unstable power supply, and the problem of energy waste when renewable energy is in excess, and the problem of insufficient power supply when power demand increases, traditional energy storage methods such as pumped storage and compressed air storage have problems of low efficiency and slow response. The existing energy storage and energy supply systems may cause system instability or safety accidents due to equipment failure or abnormal operation. Please refer to Figure 1-2 , the present invention provides the following technical solutions: The power supply system based on hydrogen and ammonia energy storage includes: Hydrogen generation unit for: Electrolyze water through an electrolyzer and extract hydrogen from the water, remove impurities from the extracted hydrogen through a purifier, and store or transport it to a hydrogen-ammonia battery to provide hydrogen supply; Ammonia generating unit, used for: The hydrogen and nitrogen are synthesized by a Haber-Bosch synthesis device to synthesize ammonia and the ammonia is dissolved in water to form an ammonia solution, which is stored in an ammonia solution storage tank for standby use; Electrical energy storage unit for: When renewable energy generation exceeds demand, the excess electricity is input into the lithium-ion battery energy storage device through the charge and discharge control system to store the excess electricity. When the demand for electricity increases, the charge and discharge control system releases the electricity from the lithium-ion battery energy storage device to supply the grid. Hydrogen-ammonia battery cells for: The hydrogen gas and ammonia solution are combined at the anode and cathode of the hydrogen-ammonia battery stack, and the electrons generated by the hydrogen-ammonia battery stack flow through the external circuit to form an electric current, driving the load to work or connecting to the grid for power generation; Intelligent control module for: Monitor the operating status of the hydrogen generation unit, ammonia generation unit, electric energy storage unit and hydrogen-ammonia battery unit, adjust the production and storage ratio of hydrogen and ammonia, and control the balance of electricity supply and demand.

[0019] In the above embodiment, hydrogen is generated by electrolyzing water, and hydrogen and nitrogen are combined to form ammonia. This process converts intermittent renewable energy (such as solar energy and wind energy) into chemical energy (hydrogen and ammonia water) that can be stored for a long time. This storage method avoids the problem of unstable power supply caused by the intermittent and volatile nature of renewable energy. The hydrogen-ammonia battery unit directly converts the chemical energy in hydrogen and ammonia solutions into electrical energy with high conversion efficiency. It can also quickly release electrical energy when needed to meet the load requirements of the power grid.

[0020] In the above embodiment, the energy storage unit in the system can store electricity when there is excess electricity generation from renewable energy and release electricity when electricity demand increases, thereby effectively balancing the supply and demand relationship and reducing energy waste. The synergistic effect of the hydrogen generation unit, the ammonia generation unit and the energy storage unit enables the system to flexibly adjust the operating status of each unit according to the real-time electricity demand and renewable energy generation situation, thereby improving the overall energy utilization rate.

[0021] In the above embodiment, the application of the intelligent control module enables the system to monitor the operating status of each unit in real time, promptly detect and handle abnormal situations, and ensure the safe and stable operation of the system. Through data analysis and prediction, the intelligent control module can formulate the optimal energy management strategy to achieve optimal configuration and efficient utilization of energy.

[0022] The hydrogen generation unit includes an electrolyzer, a DC power supply, a gas separation device, a purifier, and a high-pressure gas tank; The DC power supply is connected to the electrolytic cell, which is connected to the gas separation device. The gas separation device is used to separate the gas generated by the electrolytic cell. The separated hydrogen enters the purifier. The purifier is used to purify the hydrogen. The purified hydrogen enters the high-pressure gas tank for storage or is transported to the ammonia generation unit through a pipeline.

[0023] In the above embodiment, the hydrogen generation unit generates hydrogen by electrolyzing water, which is a renewable process. As long as there is sufficient water and electricity supply, hydrogen can be continuously generated to provide energy for subsequent ammonia synthesis and hydrogen-ammonia batteries.

[0024] In the above embodiment, during the electrolysis of water, the electrical energy provided by the DC power supply is converted into the chemical energy of hydrogen, which is an efficient way of energy conversion. At the same time, through the gas separation and hydrogen purification steps, the purity and quality of hydrogen are ensured, and efficient reactants are provided for subsequent reactions. When the power grid has surplus electricity, the power of the electrolyzer can be quickly adjusted to increase the output of hydrogen and store it for emergency use. At the same time, when necessary, hydrogen can be quickly transported to the ammonia generation unit or the hydrogen-ammonia battery unit through a pipeline. By optimizing the operating conditions and purification process of the electrolyzer, energy losses can be reduced, the output and purity of hydrogen can be increased, and the energy utilization rate of the entire system can be improved.

[0025] The ammonia generation unit includes a nitrogen and hydrogen supply system, a Haber-Bosch synthesis unit, an ammonia preparation system, and an ammonia solution storage tank; Among them, the nitrogen and hydrogen supply system is used to input nitrogen and hydrogen to the Haber-Bosch synthesis device. The hydrogen is connected to the Haber-Bosch synthesis device from the hydrogen generation unit through a pipeline. The nitrogen is separated from the air and then input into the Haber-Bosch synthesis device. The Haber-Bosch synthesis device is used to synthesize ammonia. The generated ammonia enters the ammonia water preparation system to react with water to generate ammonia water, and the generated ammonia water is stored in an ammonia solution storage tank.

[0026] In the above embodiment, the ammonia generating unit synthesizes ammonia using hydrogen and nitrogen in the air, and then dissolves the ammonia in water to form an ammonia solution. This is a sustainable cycle process. As long as the supply of hydrogen and nitrogen is stable, ammonia can be continuously produced.

[0027] In the above embodiment, the Haber-Bosch synthesis unit catalyzes the synthesis of ammonia, which is an efficient chemical reaction process, and the ammonia water preparation system converts ammonia into an ammonia water solution for easy storage and transportation. When it is necessary to quickly increase the production of ammonia water, the operating conditions of the Haber-Bosch synthesis unit can be adjusted, such as increasing the temperature, pressure or catalyst dosage, to accelerate the synthesis of ammonia. By optimizing the operating parameters of the Haber-Bosch synthesis unit and the ammonia water preparation process, energy consumption and by-product generation can be reduced, and the yield and purity of ammonia water can be improved.

[0028] The electric energy storage unit includes a lithium-ion battery energy storage device and a charge and discharge control system; The charge and discharge control system is connected to a lithium-ion battery energy storage device, and is used to control the charge and discharge state of the lithium-ion battery energy storage device, and the lithium-ion battery energy storage device is connected to a power grid.

[0029] In the above embodiment, the energy storage unit stores excess energy when renewable energy generation exceeds demand, and releases energy to supply the power grid when power demand increases, effectively balancing the supply and demand relationship and ensuring a continuous supply of energy. Lithium-ion battery energy storage equipment has high energy density and efficient charging and discharging performance, and can quickly respond to load changes in the power grid to achieve efficient use of energy. The charge and discharge control system can quickly adjust the charge and discharge state of the lithium-ion battery to adapt to the real-time needs of the power grid. By intelligently managing the charge and discharge process of lithium-ion batteries, battery loss and energy waste can be reduced, and the overall utilization of energy can be improved.

[0030] The hydrogen-ammonia battery unit includes a hydrogen-ammonia battery stack including a plurality of membrane electrode assemblies, a fuel supply system, and an energy conversion system; Among them, the fuel supply system is used to transport hydrogen and ammonia solution to the anode and cathode of the hydrogen-ammonia battery stack respectively. The fuel supply system is provided with a hydrogen pipeline and an ammonia pipeline. The hydrogen pipeline is connected from the hydrogen generation unit to the anode side of the hydrogen-ammonia battery stack, and the ammonia pipeline is connected from the ammonia generation unit to the cathode side of the hydrogen-ammonia battery stack. The energy conversion system is used to output the electrical energy generated by the chemical reaction of the hydrogen-ammonia battery stack to the power grid or load.

[0031] In the above embodiment, the hydrogen-ammonia battery unit utilizes the stored hydrogen gas and ammonia aqueous solution to undergo a chemical reaction to generate electrical energy, which is a clean and efficient energy conversion method that can provide a continuous power supply to the power grid or load.

[0032] In the above embodiment, the multiple membrane electrode assemblies in the hydrogen-ammonia battery stack can efficiently catalyze the chemical reaction of hydrogen and ammonia solution to generate a large amount of electrical energy. At the same time, the energy conversion system can efficiently output the generated electrical energy to the power grid or load. The hydrogen-ammonia battery stack has a fast reaction speed and can quickly respond to load changes in the power grid or the power demand of the load. By optimizing the operating conditions of the hydrogen-ammonia battery stack and the performance of the membrane electrode assembly, the power generation efficiency and stability of the battery can be improved, thereby improving the energy utilization rate of the entire system.

[0033] Intelligent control module, including: Data acquisition and monitoring unit, used for: The operating status data of the hydrogen generation unit, ammonia generation unit, electric energy storage unit and hydrogen-ammonia battery unit are collected in real time through sensors arranged in each subsystem. The operating status data includes temperature, pressure, gas flow, liquid flow and electricity. The sensors include temperature sensors, pressure sensors, flow meters and electricity meters, which display various parameters in real time, monitor the load changes of the power grid and the power generation status of renewable energy, and store historical data records; Specifically, the data collection and monitoring unit includes: A real-time monitoring module is used to monitor in real time the data collection response time corresponding to each data collection of each sensor; A data acquisition characterization coefficient acquisition module is used to acquire a data acquisition characterization coefficient corresponding to each sensor according to a data acquisition response time corresponding to each sensor for each data acquisition; The data acquisition characterization coefficient is obtained by the following formula: ; Where F represents the data acquisition characterization coefficient; n represents the number of times the sensor collects data; T i represents the data collection response time corresponding to the i-th data collection; T e Indicates the maximum collection response time allowed for normal data collection operation; T zand T p Indicates the median and average values ​​of the data collection response time corresponding to n data collections; A first comparison module, used for comparing the data acquisition characterization coefficient corresponding to each sensor with a preset characterization coefficient threshold; An abnormal alarm module is used to issue a sensor data acquisition abnormal alarm when the data acquisition characterization coefficient corresponding to the sensor is lower than a preset characterization coefficient threshold; The sensor network quality detection module is used to perform operation quality detection on the operation of the entire sensor network using the data acquisition characterization coefficient corresponding to each sensor when the data acquisition characterization coefficients corresponding to the sensors are not lower than a preset characterization coefficient threshold.

[0034] The technical effect of the above technical solution is: through the real-time monitoring module, the system can continuously track the response time of each sensor during the data acquisition process. This real-time monitoring mechanism helps to promptly discover potential performance problems or failures and ensure the continuity and accuracy of data acquisition. The data acquisition characterization coefficient acquisition module uses the above formula to calculate the data acquisition characterization coefficient of each sensor. This coefficient comprehensively considers multiple factors such as the response time of data acquisition, the maximum acquisition response time allowed for normal operation, the median and average values ​​of the response time. In this way, the system can comprehensively evaluate the data acquisition quality of the sensor, rather than relying solely on a single response time indicator.

[0035] The first comparison module and the abnormal alarm module together constitute the fault warning mechanism of the system. When the data acquisition characterization coefficient of a sensor is lower than the preset characterization coefficient threshold, the system will trigger an alarm to prompt the operator to pay attention and take corresponding maintenance measures. This helps to avoid the impact of data acquisition interruption or data quality degradation on subsequent analysis and decision-making. When the sensor data acquisition characterization coefficients all meet the preset threshold requirements, the sensor network quality detection module uses these coefficients to detect the operating quality of the entire sensor network. This helps system administrators understand the health of the entire network and promptly discover and solve problems that may affect the overall performance of the network. Through the collaborative work of the above functional modules, this technical solution can significantly improve the stability and reliability of the data acquisition system. Real-time monitoring and fault warning mechanisms help prevent potential problems, while quantitative evaluation of data acquisition quality provides an accurate performance monitoring method. These measures together ensure the smooth progress of the data acquisition process and the accuracy of the data.

[0036] In summary, this technical solution effectively improves the stability and reliability of the data acquisition system through real-time monitoring, quantitative evaluation, fault warning and network quality detection, providing strong support for subsequent data analysis and decision-making.

[0037] Specifically, the sensor network quality detection module includes: A data acquisition characterization coefficient retrieving module, used for retrieving the data acquisition characterization coefficient corresponding to each sensor when the data acquisition characterization coefficients corresponding to the sensors are not lower than a preset characterization coefficient threshold; A coefficient threshold retrieving module, used for retrieving a first coefficient threshold and a second coefficient threshold from a database; A second comparison module, configured to compare the data acquisition characterization coefficient with the first coefficient threshold and a second coefficient threshold; A first data acquisition characterization coefficient extraction module, used to extract the data acquisition characterization coefficient corresponding to the sensor exceeding the first coefficient threshold; A first comprehensive coefficient parameter acquisition module, used to acquire a first comprehensive coefficient parameter using the data acquisition characterization coefficient corresponding to the sensor exceeding the first coefficient threshold; The first comprehensive coefficient parameter is obtained by the following formula: ; Among them, S 01 represents the first comprehensive coefficient parameter; x represents the number of sensors whose data acquisition characterization coefficient exceeds the first coefficient threshold; F 01i represents the data acquisition characterization coefficient of the i-th sensor that exceeds the first coefficient threshold; F y01 and F y02 represents the first coefficient threshold and the second coefficient threshold; F y Indicates the preset characterization coefficient threshold; S b01 represents the first coefficient fluctuation parameter, and the first coefficient fluctuation parameter is obtained by the following formula: ; Among them, S b represents the first coefficient fluctuation parameter; x represents the number of sensors whose data acquisition characterization coefficient exceeds the first coefficient threshold; F 01i represents the data acquisition characterization coefficient of the i-th sensor that exceeds the first coefficient threshold; F y01 represents the first coefficient threshold; sqrt represents the square root function symbol; F b Indicates the standard deviation of the data acquisition characterization coefficients corresponding to all sensors; F y represents a preset characterization coefficient threshold; A second data acquisition characterization coefficient extraction module, used to extract data acquisition characterization coefficients corresponding to sensors that do not exceed the first coefficient threshold but exceed the second coefficient threshold; A second comprehensive coefficient parameter acquisition module, used to acquire a second comprehensive coefficient parameter using a data acquisition characterization coefficient corresponding to a sensor that does not exceed the first coefficient threshold but exceeds the second coefficient threshold; The second comprehensive coefficient parameter is obtained by the following formula: ; Among them, S 02 represents the second comprehensive coefficient parameter; y represents that the data acquisition characterization coefficient does not exceed the first coefficient threshold, but the number of sensors that exceeds the second coefficient threshold; F 02i represents the data acquisition characterization coefficient of the i-th sensor that does not exceed the first coefficient threshold, but exceeds the second coefficient threshold; F b Indicates the standard deviation of the data acquisition characterization coefficients corresponding to all sensors; F p Represents the average value of the data acquisition characterization coefficients corresponding to all sensors; F y02 represents the second coefficient threshold; F 02b Indicates that the standard deviation of the data acquisition characterization coefficient of the sensor does not exceed the first coefficient threshold, but exceeds the second coefficient threshold; F 01b representing a standard deviation of a data collection characterization coefficient of a sensor exceeding a first coefficient threshold; The quality detection module is used to perform operation quality detection on the operation of the entire sensor network using the first comprehensive coefficient parameter and the second comprehensive coefficient parameter.

[0038] The technical effect of the above technical solution is: by subdividing the comparison interval of the data acquisition characterization coefficient (i.e., the first coefficient threshold and the second coefficient threshold), the solution can more finely evaluate the operating status of the sensor network. This helps to find sensors that have not reached serious abnormalities but have degraded performance, so as to take measures in advance for maintenance or optimization. By introducing the first comprehensive coefficient parameter and the second comprehensive coefficient parameter, the solution can comprehensively consider the data acquisition characterization coefficients of multiple sensors, thereby more comprehensively evaluating the operating quality of the entire sensor network. This comprehensive evaluation method is more accurate and comprehensive than the performance evaluation of a single sensor.

[0039] The introduction of the first coefficient fluctuation parameter enables the scheme to evaluate the degree of dispersion of the data acquisition characterization coefficient of sensors that exceed the first coefficient threshold. This helps to find sensors that have high data acquisition characterization coefficients but large fluctuations, thereby evaluating their stability. The scheme can adopt different processing strategies based on sensors with different performance levels (i.e., sensors that exceed the first coefficient threshold, sensors that do not exceed the first coefficient threshold but exceed the second coefficient threshold, and sensors that are below the second coefficient threshold). This flexibility helps to manage the sensor network more effectively and ensure the stability and reliability of the entire network. By calculating the first comprehensive coefficient parameter and the second comprehensive coefficient parameter, the scheme provides system administrators with quantitative data on the operating quality of the sensor network. These data can be used as a basis for formulating maintenance plans, optimizing sensor configurations, or troubleshooting, which helps to improve the operating efficiency and reliability of the entire system.

[0040] In summary, this technical solution effectively improves the ability and accuracy of sensor network quality detection through refined quality detection, improved comprehensive evaluation capabilities, introduction of fluctuating parameters, flexible response to sensors with different performance levels, and provision of data support and decision-making basis, providing a strong guarantee for the stable operation of the system.

[0041] Specifically, the operation process of the quality inspection module includes: Retrieve the first comprehensive coefficient parameter and the second comprehensive coefficient parameter; Retrieving a data acquisition characterization coefficient corresponding to a second coefficient threshold value that does not exceed the second coefficient threshold value; Obtaining a comprehensive coefficient parameter by using the first comprehensive coefficient parameter and the second comprehensive coefficient parameter in combination with a data acquisition characterization coefficient corresponding to a second coefficient threshold value that does not exceed the first comprehensive coefficient parameter; The comprehensive coefficient parameter is obtained by the following formula: ; Among them, S represents the comprehensive coefficient parameter; S 01 Represents the first comprehensive coefficient parameter; S 02 Represents the second comprehensive coefficient parameter; F 01max represents the maximum value of the data acquisition characterization coefficient of x sensors exceeding the first coefficient threshold; F 02max represents the maximum value of the data acquisition coefficient of y sensors that do not exceed the first coefficient threshold, but exceed the second coefficient threshold; F z Represents the median value of the data acquisition characterization coefficients corresponding to all sensors; F z Indicates that the middle value of the data acquisition characterization coefficient corresponding to the second coefficient threshold value is not exceeded; F 03b Indicates that the standard deviation of the data acquisition characterization coefficient corresponding to the second coefficient threshold is not exceeded; F 03max Indicates that the maximum value of the data acquisition characterization coefficient corresponding to the second coefficient threshold value is not exceeded; Comparing the comprehensive coefficient parameter with a preset comprehensive coefficient threshold; When the comprehensive coefficient parameter exceeds a preset comprehensive coefficient threshold, it is determined that the sensor network operation is abnormal, and an abnormal alarm is issued.

[0042] The technical effect of the above technical solution is: the solution can more comprehensively evaluate the operating status of the sensor network by integrating multiple coefficient parameters (the first comprehensive coefficient parameter, the second comprehensive coefficient parameter, and the data acquisition characterization coefficient that does not exceed the second coefficient threshold). This comprehensive evaluation method helps to discover potential problems and ensure the stability and reliability of the entire network. By comparing the comprehensive coefficient parameter with the preset comprehensive coefficient threshold, the solution can accurately detect abnormalities in the operation of the sensor network. When the comprehensive coefficient parameter exceeds the preset threshold, the system will trigger an abnormal alarm to prompt the operator to pay attention and take corresponding maintenance measures. This helps to solve the problem in time and avoid the expansion of the fault. Once an abnormality is detected, the solution can immediately issue an abnormal alarm and provide timely fault information to the operator. This helps the operator to quickly locate the problem and take corresponding measures to repair it. This efficient exception handling mechanism can reduce the impact of the fault on the normal operation of the system. By real-time monitoring and evaluating the operating status of the sensor network, the solution can provide system administrators with data support on sensor performance. These data can be used as a basis for formulating maintenance plans, optimizing sensor configurations or troubleshooting, which helps to improve the operating efficiency and reliability of the entire system. Through comprehensive operational status assessment and accurate anomaly detection, the solution can promptly identify and resolve potential problems, thereby ensuring the stable operation of the sensor network. This stability is critical for systems that rely on sensor data for decision-making and control.

[0043] In summary, this technical solution effectively improves the ability and accuracy of sensor network quality detection through comprehensive operation status evaluation, accurate anomaly detection, efficient anomaly handling, optimized resource allocation and improved system stability, providing a strong guarantee for the stable operation of the system.

[0044] Condition Assessment Diagnostic Unit for: Set the normal operating range and threshold of the operating status parameters, evaluate the health status and performance of the hydrogen generation unit, ammonia generation unit, power storage unit and hydrogen-ammonia battery unit based on the collected operating status data, process the real-time data based on the anomaly detection algorithm, identify abnormal situations, and immediately trigger the alarm mechanism once an abnormality is detected, and notify the management personnel through the user interface and remote communication methods; Energy management and dispatching unit, used for: Formulate energy management strategies and set power demand priorities, where meeting immediate power demand takes precedence over energy storage and hydrogen / ammonia production. Forecast future power demand and renewable energy generation based on time series analysis, and dynamically adjust the operating status of each subsystem based on forecast results and real-time data; Among them, dynamically adjusting the operating status of each subsystem includes adjusting the power of the electrolyzer and the Haber-Bosch synthesis device, and controlling the charge and discharge rate of the lithium-ion battery; Intelligent control execution unit for: Receive instructions from the energy management dispatch unit and directly control the hardware equipment of the hydrogen generation unit, ammonia generation unit, electric energy storage unit and hydrogen-ammonia battery unit.

[0045] In the above embodiment, the operating status data of each subsystem is collected in real time through the data acquisition monitoring unit, and an abnormal detection algorithm is set to promptly detect potential operating failures or performance degradations, and quickly notify management personnel through the alarm mechanism to ensure stable operation of the system and avoid energy supply interruptions caused by failures. The energy management scheduling unit can predict future power demand and renewable energy generation based on time series analysis, and dynamically adjust the operating status of each subsystem according to the prediction results. When renewable energy generation is insufficient, immediate power demand can be met first, while optimizing energy storage and hydrogen / ammonia production strategies to ensure the system's continuous power supply capabilities under different load demands.

[0046] In the above embodiment, the state assessment and diagnosis unit can identify inefficient or redundant operating links by evaluating the health status and performance of each subsystem, and provide a basis for optimizing resource allocation for the energy management and scheduling unit. For example, when hydrogen or ammonia is overproduced, the corresponding production volume can be reduced, and energy storage or direct power supply can be increased to achieve efficient use of energy. The intelligent control execution unit can accurately execute instructions from the energy management and scheduling unit, accurately control the hardware equipment of each subsystem, and reduce energy waste caused by overproduction or improper operation. For example, when the power demand is low, the power of the electrolyzer and the Haber-Bosch synthesis device can be reduced to reduce energy consumption.

[0047] In the above embodiment, the intelligent control module has a fast response mechanism, which can quickly adjust the operating status of each subsystem when the grid load suddenly changes. For example, when the power demand surges, the discharge rate of the lithium-ion battery can be immediately increased or the hydrogen-ammonia battery unit can be started for grid-connected power generation to meet the immediate power demand. Through the coordination of the intelligent control module, each subsystem can work together to achieve rapid conversion and efficient utilization of energy. For example, when there is an excess of renewable energy generation, the energy storage unit can quickly absorb excess electricity and release it to the grid or hydrogen-ammonia battery unit for the production of hydrogen or ammonia when needed.

[0048] In the above embodiment, the intelligent control module comprehensively considers the operating status and data of the hydrogen generation unit, the ammonia generation unit, the electric energy storage unit and the hydrogen-ammonia battery unit, and improves the energy utilization rate of the entire system through a comprehensive optimization strategy. For example, the yield and purity of hydrogen are improved by adjusting the power of the electrolyzer and the efficiency of the purifier; the synthesis efficiency of ammonia is improved by optimizing the operating conditions and the amount of catalyst of the Haber-Bosch synthesis unit. The intelligent control module has the ability of adaptive adjustment, and can dynamically adjust the operating status and parameter settings of each subsystem according to real-time data and prediction results. This adaptive adjustment mechanism can ensure that the system always maintains a high energy utilization rate under different operating conditions and load requirements.

[0049] In order to better demonstrate the electric energy supply system based on hydrogen-ammonia energy storage, this embodiment now proposes an electric energy supply method based on hydrogen-ammonia energy storage, which is applied to the above-mentioned electric energy supply system based on hydrogen-ammonia energy storage, and includes the following steps: System initialization monitoring, start the intelligent control module, initialize all sensors and actuators, and monitor the operating status of the hydrogen generation unit, ammonia generation unit, power storage unit, and hydrogen-ammonia battery unit in real time; Energy generation and distribution, using renewable energy to generate electricity, allocating power according to real-time power demand, and using excess electricity for the production of hydrogen and ammonia; Hydrogen and ammonia production: electrolysis of water produces hydrogen, which is purified by a purifier and stored. The purified hydrogen is combined with nitrogen to form ammonia, which is then dissolved in water to form an ammonia solution for storage; Electric energy storage and release: when renewable energy generation exceeds demand, excess electric energy is stored in lithium-ion battery energy storage devices through a charge and discharge control system. When electricity demand increases, the electric energy in the lithium-ion battery energy storage devices is released to supply the grid. Hydrogen-ammonia battery power supply: When the lithium-ion battery energy storage device is short of power or the demand is at a peak, the hydrogen-ammonia battery unit is started to use the stored hydrogen and ammonia solution to react chemically in the hydrogen-ammonia battery stack to generate electricity, which is then output to the grid or load through the energy conversion system; Intelligent management optimization: the intelligent control module dynamically adjusts system operating parameters based on real-time data and historical records, and optimizes the balance of power supply and demand through the energy management and dispatching unit The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. The power supply system based on hydrogen and ammonia energy storage is characterized by: include: Hydrogen generation unit for: Electrolyze water through an electrolyzer and extract hydrogen from the water, remove impurities from the extracted hydrogen through a purifier, and store or transport it to a hydrogen-ammonia battery to provide hydrogen supply; Ammonia generating unit, used for: The hydrogen and nitrogen are synthesized by a Haber-Bosch synthesis device to synthesize ammonia and the ammonia is dissolved in water to form an ammonia solution, which is stored in an ammonia solution storage tank for standby use; Electrical energy storage unit for: When renewable energy generation exceeds demand, the excess electricity is input into the lithium-ion battery energy storage device through the charge and discharge control system to store the excess electricity. When the demand for electricity increases, the charge and discharge control system releases the electricity from the lithium-ion battery energy storage device to supply the grid. Hydrogen-ammonia battery cells for: The hydrogen gas and ammonia solution are combined at the anode and cathode of the hydrogen-ammonia battery stack, and the electrons generated by the hydrogen-ammonia battery stack flow through the external circuit to form current, drive the load to work or connect to the grid to generate electricity; Intelligent control module for: Monitor the operating status of the hydrogen generation unit, ammonia generation unit, electric energy storage unit and hydrogen-ammonia battery unit, adjust the production and storage ratio of hydrogen and ammonia, and control the balance of electricity supply and demand.

2. The electric energy supply system based on hydrogen and ammonia energy storage according to claim 1, characterized in that: The hydrogen generation unit comprises an electrolyzer, a DC power supply, a gas separation device, a purifier and a high-pressure gas tank; The DC power supply is connected to the electrolytic cell, which is connected to the gas separation device. The gas separation device is used to separate the gas generated by the electrolytic cell. The separated hydrogen enters the purifier. The purifier is used to purify the hydrogen. The purified hydrogen enters the high-pressure gas tank for storage or is transported to the ammonia generation unit through a pipeline.

3. The electric energy supply system based on hydrogen and ammonia energy storage according to claim 1, characterized in that: The ammonia water generating unit comprises a nitrogen and hydrogen supply system, a Haber-Bosch synthesis device, an ammonia water preparation system and an ammonia solution storage tank; Among them, the nitrogen and hydrogen supply system is used to input nitrogen and hydrogen to the Haber-Bosch synthesis device. The hydrogen is connected to the Haber-Bosch synthesis device from the hydrogen generation unit through a pipeline. The nitrogen is separated from the air and then input into the Haber-Bosch synthesis device. The Haber-Bosch synthesis device is used to synthesize ammonia. The generated ammonia enters the ammonia water preparation system to react with water to generate an ammonia solution. The generated ammonia solution is stored in an ammonia solution storage tank.

4. The electric energy supply system based on hydrogen and ammonia energy storage according to claim 1, characterized in that: The electric energy storage unit includes a lithium-ion battery energy storage device and a charge and discharge control system; Wherein, the charge and discharge control system is connected to the lithium-ion battery energy storage device, and the charge and discharge control system is used to control the charge and discharge state of the lithium-ion battery energy storage device, and the lithium-ion battery energy storage device is connected to the power grid; The hydrogen-ammonia battery unit includes a hydrogen-ammonia battery stack including a plurality of membrane electrode assemblies, a fuel supply system, and an energy conversion system; Among them, the fuel supply system is used to transport hydrogen and ammonia solution to the anode and cathode of the hydrogen-ammonia battery stack respectively. The fuel supply system is provided with a hydrogen pipeline and an ammonia pipeline. The hydrogen pipeline is connected from the hydrogen generation unit to the anode side of the hydrogen-ammonia battery stack, and the ammonia pipeline is connected from the ammonia generation unit to the cathode side of the hydrogen-ammonia battery stack. The energy conversion system is used to output the electrical energy generated by the chemical reaction of the hydrogen-ammonia battery stack to the power grid or load.

5. The electric energy supply system based on hydrogen and ammonia energy storage according to claim 1, characterized in that: The intelligent control module comprises: Data acquisition and monitoring unit, used for: The operating status data of the hydrogen generation unit, ammonia generation unit, electric energy storage unit and hydrogen-ammonia battery unit are collected in real time through sensors arranged in each subsystem. The operating status data includes temperature, pressure, gas flow, liquid flow and electricity. The sensors include temperature sensors, pressure sensors, flow meters and electricity meters, which display various parameters in real time, monitor the load changes of the power grid and the power generation status of renewable energy, and store historical data records; Condition Assessment Diagnostic Unit for: Set the normal working range and threshold of the operating status parameters, evaluate the health status and performance of the hydrogen generation unit, ammonia generation unit, power storage unit and hydrogen-ammonia battery unit based on the collected operating status data, process the real-time data based on the anomaly detection algorithm, identify abnormal situations, detect anomalies, immediately trigger the alarm mechanism, and notify the management personnel through the user interface and remote communication.

6. The electric energy supply system based on hydrogen and ammonia energy storage according to claim 5, characterized in that: Data acquisition and monitoring unit, including: A real-time monitoring module is used to monitor in real time the data collection response time corresponding to each data collection of each sensor; A data acquisition characterization coefficient acquisition module is used to acquire a data acquisition characterization coefficient corresponding to each sensor according to a data acquisition response time corresponding to each sensor for each data acquisition; The data acquisition characterization coefficient is obtained by the following formula: ； Where F represents the data acquisition characterization coefficient; n represents the number of times the sensor collects data; T i represents the data collection response time corresponding to the i-th data collection; T e Indicates the maximum collection response time allowed for normal data collection operation; T z and T p Indicates the median and average values ​​of the data collection response time corresponding to n data collections; A first comparison module, used for comparing the data acquisition characterization coefficient corresponding to each sensor with a preset characterization coefficient threshold; An abnormal alarm module is used to issue a sensor data acquisition abnormal alarm when the data acquisition characterization coefficient corresponding to the sensor is lower than a preset characterization coefficient threshold; The sensor network quality detection module is used to perform operation quality detection on the operation of the entire sensor network using the data acquisition characterization coefficient corresponding to each sensor when the data acquisition characterization coefficients corresponding to the sensors are not lower than a preset characterization coefficient threshold.

7. The electric energy supply system based on hydrogen and ammonia energy storage according to claim 6, characterized in that: The sensor network quality detection module includes: A data acquisition characterization coefficient retrieving module, used for retrieving the data acquisition characterization coefficient corresponding to each sensor when the data acquisition characterization coefficients corresponding to the sensors are not lower than a preset characterization coefficient threshold; A coefficient threshold retrieving module, used for retrieving a first coefficient threshold and a second coefficient threshold from a database; A second comparison module, configured to compare the data acquisition characterization coefficient with the first coefficient threshold and a second coefficient threshold; A first data acquisition characterization coefficient extraction module, used to extract the data acquisition characterization coefficient corresponding to the sensor exceeding the first coefficient threshold; A first comprehensive coefficient parameter acquisition module, used to acquire a first comprehensive coefficient parameter using the data acquisition characterization coefficient corresponding to the sensor exceeding the first coefficient threshold; The first comprehensive coefficient parameter is obtained by the following formula: ； Among them, S 01 represents the first comprehensive coefficient parameter; x represents the number of sensors whose data acquisition characterization coefficient exceeds the first coefficient threshold; F 01i represents the data acquisition characterization coefficient of the i-th sensor that exceeds the first coefficient threshold; F y01 and F y02 represents the first coefficient threshold and the second coefficient threshold; F y Indicates the preset characterization coefficient threshold; S b represents the first coefficient fluctuation parameter, and the first coefficient fluctuation parameter is obtained by the following formula: ； Among them, S b represents the first coefficient fluctuation parameter; x represents the number of sensors whose data acquisition characterization coefficient exceeds the first coefficient threshold; F 01i represents the data acquisition characterization coefficient of the i-th sensor that exceeds the first coefficient threshold; F y01 represents the first coefficient threshold; sqrt represents the square root function symbol; F b Indicates the standard deviation of the data acquisition characterization coefficients corresponding to all sensors; F y represents a preset characterization coefficient threshold; A second data acquisition characterization coefficient extraction module, used to extract data acquisition characterization coefficients corresponding to sensors that do not exceed the first coefficient threshold but exceed the second coefficient threshold; A second comprehensive coefficient parameter acquisition module, used to acquire a second comprehensive coefficient parameter using a data acquisition characterization coefficient corresponding to a sensor that does not exceed the first coefficient threshold but exceeds the second coefficient threshold; The second comprehensive coefficient parameter is obtained by the following formula: ； Among them, S 02 represents the second comprehensive coefficient parameter; y represents that the data acquisition characterization coefficient does not exceed the first coefficient threshold, but the number of sensors that exceeds the second coefficient threshold; F 02i represents the data acquisition characterization coefficient of the i-th sensor that does not exceed the first coefficient threshold, but exceeds the second coefficient threshold; F b Indicates the standard deviation of the data acquisition characterization coefficients corresponding to all sensors; F p Represents the average value of the data acquisition characterization coefficients corresponding to all sensors; F y02 represents the second coefficient threshold; F 02b Indicates that the standard deviation of the data acquisition characterization coefficient of the sensor does not exceed the first coefficient threshold, but exceeds the second coefficient threshold; F 01b representing a standard deviation of a data collection characterization coefficient of a sensor exceeding a first coefficient threshold; The quality detection module is used to perform operation quality detection on the operation of the entire sensor network using the first comprehensive coefficient parameter and the second comprehensive coefficient parameter.

8. The electric energy supply system based on hydrogen and ammonia energy storage according to claim 7 is characterized in that: The operation process of the quality inspection module includes: Retrieve the first comprehensive coefficient parameter and the second comprehensive coefficient parameter; Retrieving a data acquisition characterization coefficient corresponding to a second coefficient threshold value that does not exceed the second coefficient threshold value; Obtaining a comprehensive coefficient parameter by using the first comprehensive coefficient parameter and the second comprehensive coefficient parameter in combination with a data acquisition characterization coefficient corresponding to a second coefficient threshold value that does not exceed the first comprehensive coefficient parameter; The comprehensive coefficient parameter is obtained by the following formula: ； Among them, S represents the comprehensive coefficient parameter; S 01 Represents the first comprehensive coefficient parameter; S 02 Represents the second comprehensive coefficient parameter; F 01max represents the maximum value of the data acquisition characterization coefficient of x sensors exceeding the first coefficient threshold; F 02max represents the maximum value of the data acquisition coefficient of y sensors that do not exceed the first coefficient threshold, but exceed the second coefficient threshold; F z Represents the median value of the data acquisition characterization coefficients corresponding to all sensors; F z Indicates that the middle value of the data acquisition characterization coefficient corresponding to the second coefficient threshold value is not exceeded; F 03b Indicates that the standard deviation of the data acquisition characterization coefficient corresponding to the second coefficient threshold is not exceeded; F 03max Indicates that the maximum value of the data acquisition characterization coefficient corresponding to the second coefficient threshold value is not exceeded; Comparing the comprehensive coefficient parameter with a preset comprehensive coefficient threshold; When the comprehensive coefficient parameter exceeds a preset comprehensive coefficient threshold, it is determined that the sensor network operation is abnormal, and an abnormal alarm is issued.

9. The electric energy supply system based on hydrogen and ammonia energy storage according to claim 5, characterized in that: The intelligent control module further includes: Energy management and dispatching unit, used for: Formulate energy management strategies and set power demand priorities, where meeting immediate power demand takes precedence over energy storage and hydrogen / ammonia production. Forecast future power demand and renewable energy generation based on time series analysis, and dynamically adjust the operating status of each subsystem based on forecast results and real-time data; Among them, dynamically adjusting the operating status of each subsystem includes adjusting the power of the electrolyzer and the Haber-Bosch synthesis device, and controlling the charge and discharge rate of the lithium-ion battery; Intelligent control execution unit for: Receives instructions from the energy management dispatch unit and controls the hardware equipment of the hydrogen generation unit, ammonia generation unit, electric energy storage unit and hydrogen-ammonia battery unit.

10. A method for supplying electric energy based on hydrogen-ammonia energy storage, applied to a system for supplying electric energy based on hydrogen-ammonia energy storage as claimed in any one of claims 1 to 9, characterized in that: The steps include: System initialization monitoring, start the intelligent control module, initialize all sensors and actuators, and monitor the operating status of the hydrogen generation unit, ammonia generation unit, power storage unit, and hydrogen-ammonia battery unit in real time; Energy generation and distribution, using renewable energy to generate electricity, allocating power according to real-time power demand, and using excess electricity for the production of hydrogen and ammonia solutions; Hydrogen and ammonia production: electrolysis of water produces hydrogen, which is purified by a purifier and stored. The purified hydrogen is combined with nitrogen to form ammonia, which is then dissolved in water to form an ammonia solution for storage; Electric energy storage and release: when renewable energy generation exceeds demand, excess electric energy is stored in lithium-ion battery energy storage devices through a charge and discharge control system. When electricity demand increases, the electric energy in the lithium-ion battery energy storage devices is released to supply the grid. Hydrogen-ammonia battery power supply: When the lithium-ion battery energy storage device is short of power or the demand is at a peak, the hydrogen-ammonia battery unit is started to use the stored hydrogen and ammonia solution to react chemically in the hydrogen-ammonia battery stack to generate electricity, which is then output to the grid or load through the energy conversion system; Intelligent management optimization: the intelligent control module dynamically adjusts system operating parameters based on real-time data and historical records, and optimizes the balance of power supply and demand through the energy management and scheduling unit.

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