Comprehensive energy control method of micro-grid system fused with electricity, hydrogen and ammonia

Abstract

The invention discloses a comprehensive energy control method for a micro-grid system fusing electricity, hydrogen and ammonia, and relates to the technical field of power grid energy management, and the method comprises the following steps: S1, constructing a multi-energy coupling system architecture: the system comprises a power generation module composed of a photovoltaic array and a wind turbine, the system further comprises a water electrolysis hydrogen production device, a hydrogen storage tank, an ammonia synthesis reactor, a liquid ammonia storage tank, a fuel cell and a power grid interface. According to the comprehensive energy control method of the micro-grid system fusing electricity, hydrogen and ammonia, a three-stage cooperative control architecture is combined with a dynamic molar ratio compensation technology, so that the overall efficiency of hydrogen and ammonia circulation is improved, a scheduling layer is optimized to reduce hydrogen production synthesis energy loss, and the ammonia synthesis proportion is corrected in real time to maintain efficient reaction; and stable operation is guaranteed through catalyst attenuation compensation, an ammonia energy peak shifting and valley filling strategy is combined with a carbon trading mechanism, so that the cost of electricity purchase is reduced, and a remarkable cost advantage is formed through a price difference arbitrage mechanism of valley electricity hydrogen production and peak electricity energy supply in cooperation with the cross-time shifting allocation capacity of liquid ammonia energy storage.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid energy management, in particular to a comprehensive energy control method of a micro-grid system integrating electricity, hydrogen and ammonia. BACKGROUND

[0002] Traditional micro-grid mainly relies on battery energy storage, but has problems such as limited capacity, high cost and short service life. Hydrogen energy has advantages as a long-term energy storage carrier, but faces challenges in storage and transportation. Ammonia, as a hydrogen energy carrier, has the advantages of high energy density and easy liquefied storage.

[0003] A comprehensive energy control method and device of a micro-grid system integrating electricity, hydrogen and ammonia are disclosed in Chinese Patent No. CN118889561A. The micro-grid system includes a DC bus, an energy router, a photovoltaic power generation system, a hydrogen fuel cell system, an electrochemical energy storage system, and a new energy load composed of a water electrolysis hydrogen production system, an ammonia cracking hydrogen production system and a hydrogen storage system. The DC bus is connected to the external AC bus through the energy router, and the ammonia cracking hydrogen production system and the hydrogen storage system are connected to the external AC bus. The method includes: obtaining the actual output curve of the photovoltaic power generation system in the current measurement period and the planned output power of the new energy load at the current time to determine the power deviation; when the power deviation is less than zero, using the first planned output power determined according to the actual output curve to control the discharge of the micro-grid system; when the power deviation is greater than zero, using the planned output power to control the charging of the micro-grid system, which can improve the output stability of the photovoltaic power generation system.

[0004] The electricity, hydrogen and ammonia coupling system lacks a dynamic coordination mechanism, often resulting in mismatch between hydrogen production power and synthesis demand, strong hysteresis of ammonia synthesis reaction, difficulty in adapting to renewable energy fluctuations, and insufficient consideration of the interactive effects of equipment degradation and market fluctuations. SUMMARY

[0005] The present application aims to provide a comprehensive energy control method of a micro-grid system integrating electricity, hydrogen and ammonia to solve the problems of lack of dynamic coordination mechanism in the electricity, hydrogen and ammonia coupling system, frequent mismatch between hydrogen production power and synthesis demand, strong hysteresis of ammonia synthesis reaction, difficulty in adapting to renewable energy fluctuations, and insufficient consideration of the interactive effects of equipment degradation and market fluctuations in the background art.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical solution: a comprehensive energy control method of a micro-grid system integrating electricity, hydrogen and ammonia, comprising the following steps:

[0007] S1: Construct a multi-energy coupling system architecture: the system includes a power generation module composed of a photovoltaic array and a wind turbine, and also includes a water electrolysis hydrogen production device, a hydrogen storage tank, an ammonia synthesis reactor, a liquid ammonia storage tank, a fuel cell and a grid interface;

[0008] S2: Establishing energy dynamic model:

[0009] Electric energy dynamic equation:

[0010] Hydrogen storage equation:

[0011] Liquid ammonia storage equation:

[0012] Wherein, is the ammonia synthesis rate, , , electrolysis, synthesis, fuel cell efficiency, respectively;

[0013] S3: Implementing three-level coordinated control strategy:

[0014] Day-ahead scheduling layer: based on wind and light prediction and load demand, 24-hour energy distribution is optimized to minimize operating cost;

[0015] Real-time regulation layer: 5-minute cycle correction scheduling deviation, dynamic adjustment of hydrogen production power and synthesis reaction temperature;

[0016] Millisecond emergency layer: voltage / frequency support is realized through converter control;

[0017] S4: Implementing hydrogen-ammonia coupling priority strategy:

[0018] When the hydrogen storage is higher than the threshold , start ammonia synthesis device; when lower than , call liquid ammonia cracking device for hydrogen;

[0019] S5: Deploying artificial intelligence prediction correction module:

[0020] LSTM network is used to predict wind and light output fluctuation, and control parameters are dynamically updated through reinforcement learning.

[0021] Preferably, the step S3 day-ahead scheduling layer adopts mixed integer linear programming (MILP) model, and the objective function is:

[0022] ;

[0023] Wherein the constraints include:

[0024] Electric power balance constraint;

[0025] Hydrogen system climbing constraint: ;

[0026] Ammonia synthesis reaction temperature constraint: .

[0027] Preferably, the real-time regulation layer adopts model predictive control (MPC), which solves the following optimization problem in a rolling horizon:

[0028] Objective function formula: ;

[0029] Reaction kinetics equation formula: ;

[0030] SOC constraint formula: .

[0031] Preferably, the MPC controller embeds a nitrogen-hydrogen molar ratio feedback compensation module, which automatically adjusts the nitrogen gas compressor speed when the sensor detects that the synthesis gas The proportion deviates from 3:1.

[0032] Preferably, the millisecond emergency layer contains:

[0033] Voltage drop response: switch the fuel cell to constant voltage mode within 100ms;

[0034] Frequency offset response: release rotational inertia energy through the inverter ;

[0035] When a short-circuit current >10kA is detected, trigger the solid-state circuit breaker to disconnect the ammonia synthesis device power supply circuit within 5μs.

[0036] Preferably, the artificial intelligence prediction module adopts a dual-channel CNN-LSTM hybrid network, and the input data includes:

[0037] Weather data: irradiance, wind speed, cloud motion vector;

[0038] Market data: real-time electricity price, liquid ammonia futures price;

[0039] Equipment status: electrolytic cell membrane electrode health index;

[0040] Through the pre-training of the benchmark model established in the desert climate data, fine-tune to adapt to the coastal high-humidity environment.

[0041] Preferably, the reinforcement learning agent adopts the DDPG algorithm, and the reward function is designed as:

[0042] ; wherein is the adjustable capacity ratio.

[0043] Preferably, the ammonia synthesis reactor adopts a ruthenium-based catalyst, and the control system pre-sets a catalyst activity attenuation compensation coefficient λ: .

[0044] Preferably, hydrogen storage adopts a double-layer safety container structure, the inner layer is a composite material storage tank, and the outer layer is a liquid nitrogen cooling screen, the control system maintains the temperature in the tank at-253±5℃, a flash re-liquefaction device is arranged at the outlet of the liquid ammonia storage tank, the pressure is controlled at 0.8-1.2 MPa, and the scheduling model further contains a carbon trading cost term:

[0045] wherein δ is the carbon price

[0046] wherein is the power grid emission factor;

[0047] The water electrolysis device adopts a PEM and ALK dual-mode architecture, and the control strategy switches the operation mode according to the electricity price signal, starts the low-efficiency long-life ALK mode when the electricity price is <0.05 / kWh, and starts the high-efficiency quick-response PEM mode when the electricity price is >0.12 / kWh, the control system monitors the pure water conductivity and dynamically adjusts the ion membrane electric field strength, and maintains the current density at 1.5±0.2 A / cm², and an oxygen removal device is arranged at the inlet of the ammonia synthesis gas compressor, and the control system maintains the oxygen concentration <1 ppm.

[0048] Preferably, hydrogen and liquid ammonia enter the high-temperature fuel cell after being cracked to produce hydrogen, and the combined heat and power efficiency is >65%; the reactor temperature is controlled at 850±30℃, the methane mixing ratio is adjusted through infrared temperature measurement closed loop, the wind and solar power generation unit is configured with a virtual synchronous generator (VSG) control, the inertia time constant H can be adjusted in the range of 2 8s; when the frequency change rate , the inertia constant is automatically increased to the maximum value, an equipment health evaluation model is established, the maintenance plan is started in advance when the predicted electrolysis cell efficiency decay rate is >5% / year, the health evaluation is based on the acoustic emission sensor to monitor the hydrogen brittle crack propagation rate, and the control system is deployed on a blockchain platform, and each energy node control instruction is executed through a smart contract.

[0049] Compared with the prior art, the beneficial effects of the present application are:

[0050] The comprehensive energy control method of the micro-grid system fusing electricity, hydrogen and ammonia realizes the improvement of the overall efficiency of hydrogen and ammonia circulation through the three-level collaborative control architecture combined with the dynamic molar ratio compensation technology, optimizes the energy loss of hydrogen synthesis at the scheduling layer, realizes real-time correction of the ammonia synthesis ratio to maintain efficient reaction, and guarantees stable operation through catalyst decay compensation.

[0051] The peak load shifting strategy of ammonia energy combined with the carbon trading mechanism reduces the cost of purchased electricity, realizes the price difference arbitrage mechanism of hydrogen production by valley electricity and energy supply by peak electricity, cooperates with the cross-time displacement ability of liquid ammonia energy storage, and forms a significant cost advantage.

[0052] The triple guarantee of shortened voltage fault recovery time, the ability to switch fuel cell power supply mode in a short time, the rapid response of releasing rotational inertia energy when frequency fluctuates, and the blocking of short-circuit faults by microsecond solid-state circuit breakers reduces the downtime of the fault.

[0053] The predictive maintenance mechanism of the device state intervenes in advance when the efficiency attenuation begins to appear; the real-time optimization of the membrane electrode operating parameters suppresses the performance degradation; and the hydrogen embrittlement crack monitoring prevents structural damage, thereby prolonging the service life of the core equipment. BRIEF DESCRIPTION OF DRAWINGS

[0054] Figure 1 The figure is a schematic diagram of the system architecture of the present application;

[0055] Figure 2 The figure is a schematic diagram of the day-ahead scheduling layer of the present application;

[0056] Figure 3 The figure is a schematic diagram of the real-time adjustment layer of the present application;

[0057] Figure 4 The figure is a schematic diagram of the millisecond emergency layer of the present application;

[0058] Figure 5 The figure is a schematic diagram of the AI architecture of the present application. DETAILED DESCRIPTION

[0059] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0060] Embodiment one: please refer to Figure 1 - Figure 5 The present application provides the following technical solutions: a comprehensive energy control method for a micro-grid system integrating electricity, hydrogen and ammonia, comprising the following steps:

[0061] S1: Construct a multi-energy coupling system architecture: the system includes a power generation module composed of a photovoltaic array and a wind turbine, and also includes a water electrolysis hydrogen production device, a hydrogen storage tank, an ammonia synthesis reactor, a liquid ammonia storage tank, a fuel cell and a power grid interface;

[0062] S2: Establish an energy dynamic model:

[0063] Electric energy dynamic equation:

[0064] Hydrogen storage equation:

[0065] Liquid ammonia storage equation:

[0066] wherein, is the ammonia synthesis rate, , , are the electrolysis, synthesis, fuel cell efficiencies, respectively;

[0067] S3: Implement a three-level coordinated control strategy:

[0068] Day-ahead scheduling layer: Based on wind and solar prediction and load demand, optimize 24-hour energy distribution with the goal of minimizing operating costs;

[0069] Real-time regulation layer: Correct scheduling deviations every 5 minutes, dynamically adjust hydrogen production power and synthesis reaction temperature;

[0070] Millisecond emergency layer: Voltage / frequency support through converter control;

[0071] S4: Execute hydrogen-ammonia coupling priority strategy:

[0072] When the hydrogen storage amount is higher than the threshold , start the ammonia synthesis device; when it is lower than , call the liquid ammonia cracking device for hydrogen supply;

[0073] S5: Deploy an artificial intelligence prediction correction module:

[0074] Use LSTM network to predict wind and solar output fluctuations, and dynamically update control parameters through reinforcement learning.

[0075] The step S3 day-ahead scheduling layer uses a mixed integer linear programming (MILP) model, and the objective function is:

[0076] ;

[0077] The constraints include:

[0078] Electric power balance constraint;

[0079] Hydrogen system ramp constraint: ;

[0080] Ammonia synthesis reaction temperature constraint: .

[0081] The real-time regulation layer uses model predictive control (MPC) to solve the following optimization problem in a rolling time domain:

[0082] Objective function formula: ;

[0083] Reaction kinetics equation formula: ;

[0084] SOC constraint formula: .

[0085] The MPC controller embeds a molar ratio feedback compensation module for nitrogen and hydrogen. When the sensor detects that the synthesis gas deviates from the ratio of 3:1, the nitrogen compressor speed is automatically adjusted.

[0086] The millisecond emergency layer includes:

[0087] Voltage drop response: switch the fuel cell to constant voltage mode within 100ms;

[0088] Frequency offset response: release rotational inertia energy through the inverter ;

[0089] When a short-circuit current >10kA is detected, a solid-state circuit breaker is triggered to disconnect the ammonia synthesis device power supply circuit within 5μs.

[0090] The artificial intelligence prediction module uses a dual-channel CNN-LSTM hybrid network, and the input data includes:

[0091] Weather data: irradiance, wind speed, cloud motion vector;

[0092] Market data: real-time electricity price, liquid ammonia futures price;

[0093] Equipment status: electrolytic cell membrane electrode health index;

[0094] Through pre-training, a benchmark model is established based on desert climate data, and fine-tuning is adapted to coastal high-humidity environment.

[0095] The reinforcement learning agent uses the DDPG algorithm, and the reward function is designed as:

[0096] ; wherein is the dispatchable capacity ratio.

[0097] The ammonia synthesis reactor uses a ruthenium-based catalyst, and the control system presets a catalyst activity attenuation compensation coefficient λ: .

[0098] Hydrogen storage uses a double-layer safety container structure, the inner layer is a composite material storage tank, and the outer layer is a liquid nitrogen cooling screen. The control system maintains the tank temperature at -253±5℃, and a flash re-liquefaction device is set at the outlet of the liquid ammonia storage tank to control the pressure at 0.8-1.2MPa. The scheduling model further includes a carbon trading cost term: , wherein δ is the carbon price, Grid emission factor; PEM and ALK dual-mode architecture for water electrolysis device, control strategy switches operation mode according to electricity price signal, start low-efficiency long-life ALK mode when electricity price <0.05 / kWh, start high-efficiency fast-response PEM mode when electricity price >0.12 / kWh, control system monitors pure water conductivity and dynamically adjusts ion membrane electric field strength to maintain current density at 1.5±0.2A / cm², oxygen removal device is set at the inlet of ammonia synthesis gas compressor, control system maintains oxygen concentration <1ppm.

[0099] Hydrogen and liquid ammonia enter high-temperature fuel cells after hydrogen production by cracking, and the combined heat and power efficiency is >65%; control the reactor temperature at 850±30℃, adjust the methane mixing ratio through infrared temperature closed-loop regulation, configure virtual synchronous generator (VSG) control for wind and solar power generation unit, the inertia time constant H can be adjusted in the range of 2 8s; when the frequency change rate , automatically increase the inertia constant to the maximum value, establish a device health evaluation model, start the maintenance plan in advance when the predicted electrolyzer efficiency decay rate is >5% / year, the health evaluation is based on acoustic emission sensor monitoring of hydrogen brittle crack propagation rate, the control system is deployed on the blockchain platform, and each energy node control instruction is executed through the smart contract.

[0100] Dynamic coupling model: establish hydrogen-ammonia conversion equation considering electrochemical reaction kinetics, where the ammonia synthesis rate uses the modified Haber-Bosch kinetic model:

[0101]

[0102] Where k is the catalyst activity coefficient, is the ammonia adsorption equilibrium constant.

[0103] Multi-time scale control:

[0104] Day-ahead scheduling layer (as shown in Figure 2 ): optimize equipment start-stop plan with 24-hour cycle, target function includes: ;

[0105] Real-time regulation layer (as shown in Figure 3 ): optimize distribution instructions every 5 minutes .

[0106] Millisecond emergency layer (as shown in Figure 4 ): deploy FPGA-based fast control logic to achieve <10ms fault ride-through.

[0107] Intelligent prediction-correction mechanism:

[0108] Adopt Figure 5The AI architecture shown, where the LSTM prediction network inputs 72-hour wind and light data and outputs a 96-point power prediction sequence. The reinforcement learning agent corrects the weight coefficients (a, b, g) of the MPC according to the prediction bias.

[0109] Reference Figure 1 , a 10MW-level microgrid is built:

[0110] Photovoltaic array: single-crystal silicon components, peak power 6MWp;

[0111] Electrolytic cell: PEM type (2MW) / ALK type (3MW) in parallel;

[0112] Ammonia synthesis: sulfur-tolerant ruthenium-based catalyst, operating pressure 15MPa;

[0113] Hydrogen storage: carbon fiber winding storage tank, capacity 20,000Nm³;

[0114] Ammonia storage: double-layer vacuum insulated tank, capacity 500m³.

[0115] Data acquisition: SCADA system real-time reading of wind and light output, storage tank pressure, power grid frequency, etc. 500+ monitoring point data.

[0116] Decision-making:

[0117] Scheduling layer: CPLEX solver updates the schedule every 60 minutes;

[0118] Real-time layer: solve the MPC optimization problem every 5 minutes.

[0119] Instruction execution:

[0120] Send power set value to electrolytic cell through ModbusTCP; adjust synthesis reactor feed valve through PROFIBUS.

[0121] Safety monitoring:

[0122] Start emergency fan when hydrogen concentration >4% LEL; ammonia leakage >30ppm triggers the sprinkler system.

[0123] Prediction network training:

[0124] Dataset: meteorological and power generation data;

[0125] Network structure: Conv1D (64 layers) - LSTM (128 units) - Dense (48);

[0126] Learning rate: 0.001 (Adam optimizer).

[0127] Reinforcement learning training:

[0128] State space: ;

[0129] Action space: adjust (a, b, g) coefficient ± 40%;

[0130] Training period: converged after 500,000 steps.

[0131] In the description of the present application, it should be noted that unless otherwise explicitly specified and limited, the terms "connected" and "connected" should be understood broadly, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0132] Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features, any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A comprehensive energy control method for a microgrid system that integrates power hydrogen and ammonia, characterized by, The method comprises the following steps: S1: constructing a multi-energy coupling system architecture: the system comprises a power generation module composed of a photovoltaic array and a wind turbine, and further comprises a water electrolysis hydrogen production device, a hydrogen storage tank, an ammonia synthesis reactor, a liquid ammonia storage tank, a fuel cell, and a power grid interface; S2: establishing an energy dynamic model: Electrical power dynamic equation: Hydrogen storage equation: Liquid ammonia inventory equation: wherein, is the ammonia synthesis rate, , , are the electrolysis, synthesis, fuel cell efficiencies, respectively; S3: implementing a three-level coordinated control strategy: Day-ahead scheduling layer: based on wind and light prediction and load demand, the 24-hour energy distribution is optimized to minimize operating cost; Real-time regulation layer: correcting scheduling deviation every 5 minutes, dynamically adjusting hydrogen production power and synthesis reaction temperature; Millisecond emergency layer: voltage / frequency support is achieved through converter control; S4: executing a hydrogen-ammonia coupling priority strategy: When the hydrogen storage amount is higher than a threshold value , the ammonia synthesis device is started; when it is lower than , the liquid ammonia cracking device is called to supply hydrogen; S5: deploying an artificial intelligence prediction correction module: Using an LSTM network to predict wind and light output fluctuations, and dynamically updating control parameters through reinforcement learning. 2.The integrated energy control method of a microgrid system integrating power, hydrogen and ammonia according to claim 1, wherein: The step S3 day-ahead scheduling layer adopts a mixed integer linear programming (MILP) model, and the objective function is: ; The constraints include: Electric power balance constraint; Hydrogen system hill climb constraint: ; Ammonia synthesis reaction temperature constraints: . 3.The integrated energy control method of a microgrid system integrating power, hydrogen and ammonia according to claim 1, wherein: The real-time regulation layer adopts model predictive control (MPC) to solve the following optimization problem in a rolling time domain: Objective function formula: ; Reaction kinetics equation formula: ; SOC constraint equation: . 4.The integrated energy control method of a microgrid system integrating power, hydrogen and ammonia according to claim 3, wherein: The MPC controller embeds a molar ratio of nitrogen to hydrogen feedback compensation module. When the sensor detects that the synthesis gas When the proportion deviates from 3:1, the nitrogen compressor speed is automatically adjusted. 5.The integrated energy control method of a hybrid microgrid system integrating power, hydrogen and ammonia according to claim 1, wherein: The millisecond emergency layer includes: Voltage sag response: switching the fuel cell to constant voltage mode within 100 ms; Frequency offset response: release of rotational inertia energy by a variator ; When detecting a short-circuit current > 10kA, triggering a solid-state circuit breaker to disconnect the ammonia synthesis device power supply circuit within 5μs. 6.The integrated energy control method of a microgrid system integrating power, hydrogen and ammonia according to claim 1, wherein: The artificial intelligence prediction module adopts a dual-channel CNN-LSTM hybrid network, and the input data includes: Weather data: irradiance, wind speed, cloud motion vector; Market data: real-time electricity price, liquid ammonia futures price; Device status: electrolytic cell membrane electrode health index; Through pre-training, the baseline model established in the desert climate data is fine-tuned to adapt to the coastal high-humidity environment.

7. The integrated energy control method of a microgrid system integrating power, hydrogen and ammonia according to claim 6, characterized in that: The reinforcement learning agent uses the DDPG algorithm, and the reward function is designed as: ； wherein is the dispatchable capacity ratio. 8.The integrated energy control method of a hybrid microgrid system integrating power, hydrogen and ammonia according to claim 7, wherein: The ammonia synthesis reactor adopts a ruthenium-based catalyst, and a catalyst activity attenuation compensation coefficient λ is preset in the control system: . 9.The integrated energy control method of a microgrid system integrating power, hydrogen and ammonia according to claim 1, wherein: Hydrogen storage adopts a double-layer safety container structure, the inner layer is a composite material storage tank, and the outer layer is a liquid nitrogen cooling screen. The control system maintains the tank temperature at -253±5℃. A flash liquefaction device is installed at the outlet of the liquid ammonia storage tank, and the control pressure is 0.8-1.2MPa. The scheduling model further includes a carbon trading cost item: where δ is the carbon valence, is the grid emission factor; The water electrolysis device adopts a PEM and ALK dual-mode architecture, and the control strategy switches the operating mode according to the electricity price signal. When the electricity price <0.05 / kWh, the low-efficiency long-life ALK mode is started, and when the electricity price >0.12 / kWh, the high-efficiency fast-response PEM mode is started. The control system monitors the pure water conductivity and dynamically adjusts the ion membrane electric field strength to maintain the current density at 1.5±0.2A / cm². An oxygen removal device is installed at the inlet of the ammonia synthesis gas compressor, and the control system maintains the oxygen concentration <1ppm. 10.The integrated energy control method of a hybrid microgrid system integrating power, hydrogen and ammonia according to claim 9, wherein: Hydrogen is produced by cracking liquid ammonia and then enters the high-temperature fuel cell, and the combined heat and power efficiency is >65%; the reactor temperature is controlled at 850±30℃, the methane blending ratio is adjusted by infrared temperature closed-loop, the wind and solar power generation unit is configured with virtual synchronous generator (VSG) control, the inertia time constant H can be adjusted in the range of 2 8s; when the frequency change rate is changed, the inertia constant is automatically increased to the maximum value, a device health assessment model is established, when the predicted electrolytic tank efficiency decay rate is >5% / year, the maintenance plan is started in advance, the health assessment is based on acoustic emission sensor monitoring of hydrogen brittle crack propagation rate, and the control system is deployed on a blockchain platform, and each energy node control instruction is executed through a smart contract.

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