Solid oxide electrolytic hydrogen production system operation optimization control method and system

Abstract

The invention belongs to the technical field of new energy hydrogen production, and particularly relates to an operation optimization control method and system for a solid oxide electrolytic hydrogen production system, and the method comprises the steps: building a dual-time scale dynamic model of the electrolytic hydrogen production system, and outputting a thermal-electric coupling dynamic process of the solid oxide electrolytic hydrogen production system; the actual electrolysis efficiency, the accumulated thermal load and the power grid interaction power at each time are extracted, a dynamic trajectory optimization functional is constructed, and optimal control is solved; establishing a power grid auxiliary service three-dimensional decision space for primary optimization, and generating a demand decision; establishing a fuzzy control rule of the fuzzy self-adaptive PID controller to carry out secondary optimization; a dynamic safety constraint mechanism is established, and the heat flow gradient adjustment amount, the gas circulation rate and the temperature are adjusted in time; and defining a system efficiency optimization index to evaluate a system operation optimization effect. According to the method, the thermal-electric coupling dynamic process of the solid oxide electrolytic hydrogen production system can be accurately depicted at multiple time scales, and safe and stable operation of the system is guaranteed.

Description

Technical Field

[0001] The present invention belongs to the technical field of new energy hydrogen production, and in particular relates to an operation optimization control method and system for a solid oxide electrolysis hydrogen production system. Background Art

[0002] As a clean, efficient and high-energy-density secondary energy, hydrogen has important application value in many fields, such as fuel cell vehicles in the transportation field, chemical raw materials in the industrial field, and energy storage. The use of renewable energy to produce hydrogen through water electrolysis has become a hot topic in current research and application, and the solid oxide electrolysis hydrogen production system is a very promising technical means.

[0003] Most of the existing solid oxide electrolysis hydrogen production systems are operated and managed through relatively simple control strategies and conventional modeling methods. Since they mostly use a single time scale modeling method, they only consider the physical processes such as heat transfer and material diffusion in the solid oxide electrolysis hydrogen production system from a single dimension, ignoring the significant differences in the time characteristics of the thermal process and the material diffusion process. As a result, it is difficult to accurately characterize the thermal-electrical coupling dynamics of the solid oxide electrolysis hydrogen production system. Summary of the invention

[0004] In view of the above-mentioned problems, the purpose of the present invention is to provide a solid oxide electrolysis hydrogen production system operation optimization control method and system, which can accurately characterize the thermal-electric coupled dynamic process of the solid oxide electrolysis hydrogen production system at multiple time scales, thereby realizing system dynamic modeling that is more in line with actual working conditions and ensuring safe and stable operation of the system.

[0005] To achieve the above object, the present invention provides the following technical solution: a solid oxide electrolysis hydrogen production system operation optimization control method, comprising the following steps: Step 1: Establish a dual-time scale dynamic model of the electrolytic hydrogen production system and output the thermal-electric coupled dynamic process of the solid oxide electrolytic hydrogen production system; Step 2: Extract the actual electrolysis efficiency, cumulative heat load, and grid interaction power at each time from the thermal-electric coupling dynamic process, and construct a dynamic trajectory optimization functional to solve the optimal control; Step 3: After achieving optimal control, establish a three-dimensional decision space for grid auxiliary services to perform primary optimization and generate demand decisions, including: frequency regulation capacity demand, backup capacity demand, and hydrogen production priority coefficient; Step 4, establishing the fuzzy control rules of the fuzzy adaptive PID controller for secondary optimization, that is, adjusting the flow rate and gas circulation rate of the thermal management system in real time according to the electrolysis voltage deviation; Step 5: Establish a dynamic security constraint mechanism. The dynamic safety constraint mechanism is activated to adjust the heat flux gradient adjustment, gas circulation rate, and temperature. is the curvature safety threshold, is the voltage of the solid oxide electrolytic cell, is the current running time; Step 6: Extract the grid interaction power, hydrogen production power equivalent, and system abnormal status indicator at each time, and define the system efficiency optimization index to evaluate the system operation optimization effect.

[0006] Preferably, in step 1, the method for establishing a dual-time scale dynamic model of the electrolysis hydrogen production system is: Decomposing PV power fluctuations into high-frequency components and low frequency components , according to the thermal inertia time constant and the current response time constant Determine the order of the dual time scale dynamic model, where ; The dual time scale dynamic model is expressed as: ; in, is the average temperature of the electrolytic cell, is the heat loss power, is the system heat capacity, is the temperature self-regulation coefficient, is the target temperature setting value of the electrolytic cell, is the electrolysis current, is the system equivalent resistance, is the current reference value; ,in, is the density of the electrolytic cell material, is the specific heat capacity, is the volume of the electrolytic cell, is the convective heat transfer coefficient, is the effective heat exchange area; ,in, is the electrode effective diffusion coefficient, is the electrode thickness.

[0007] Preferably, in step 2, the dynamic trajectory optimization functional is defined as: ; Among them, G is the operating performance index of the solid oxide electrolysis hydrogen production system, is the end time of integration, is the starting time of integration, is the actual electrolysis efficiency, is the benchmark efficiency setting value, is the cumulative heat load, is the grid interaction power, is the power constraint threshold, is the orthographic projection operator, , , is the time-varying weight coefficient.

[0008] Preferably, the calculation formula of the time-varying weight coefficient is: ; in, is the efficiency tracking gain factor, e is a natural constant, is the thermal inertia time constant, is the power regulation factor, is the real-time photovoltaic power, is the minimum operating power threshold, is the power regulation dead zone width, is the grid interaction adjustment factor.

[0009] Preferably, in step 3, the three-dimensional decision space is defined as: ; in, For frequency modulation capacity requirements, is the grid frequency regulation demand coefficient, is the standard deviation of power fluctuation on a 10-minute scale, For spare capacity requirements, is the reserve capacity ratio coefficient, is the average power of the sliding window in the previous hour, is the hydrogen production priority coefficient, is the hydrogen storage tank capacity deviation, is the reference value of hydrogen storage tank capacity, e is a natural constant, is the grid interaction power.

[0010] Preferably, in step 4, the fuzzy control rules of the fuzzy adaptive PID controller are designed and the adjustment parameters are specifically as follows: Real-time regulation of thermal management system flow and gas circulation rate , the fuzzy control rules are: ; in, is the proportional gain coefficient, is the steady-state proportionality coefficient, is the transient proportional coefficient, is the voltage fuzzy transition threshold, is the maximum allowable voltage deviation, is a saturation function, is a symbolic function.

[0011] Preferably, the gas circulation rate The adjustment algorithm is: ; in, is the gas circulation rate at time t, is the base cycle rate, is the differential compensation gain coefficient, is the voltage change acceleration.

[0012] Preferably, in step 5, establishing a dynamic safety constraint mechanism to ensure system operation safety specifically refers to: during the entire system operation process, when Adjust parameters when Parameter adjustments include: Heat flux gradient adjustment The calculation formula is: ; The gas circulation rate is adjusted to the new gas circulation rate , the new gas circulation rate calculation formula is: ; in, is the safety attenuation coefficient, e is a natural constant, is the time interval elapsed during the run; Limiting the temperature , is the temperature coupling coefficient, is the target temperature, is the minimum temperature, is the maximum temperature, is the photovoltaic power difference before and after operation.

[0013] Preferably, in step 6, the system efficiency optimization index for: ; in, is the hydrogen production power equivalent, is the efficiency loss factor corresponding to the i-th abnormal state, is the system abnormal state indicator, i is the number of abnormal states, i∈{1,2,3}, is the grid interaction power, is the real-time photovoltaic power.

[0014] A solid oxide electrolysis hydrogen production system operation optimization control system, used to implement the above method, comprising: Model building module, used to build a dual-time scale dynamic model of the electrolytic hydrogen production system and output the thermal-electric coupled dynamic process of the solid oxide electrolytic hydrogen production system; The operation trajectory optimization module is used to extract the actual electrolysis efficiency, cumulative heat load, and grid interaction power at each time from the thermal-electric coupling dynamic process, and accordingly construct a dynamic trajectory optimization functional and solve the optimal control; The service strategy generation module is used to establish a three-dimensional decision space for power grid auxiliary services for primary optimization after achieving optimal control, and generate demand decisions, including: frequency regulation capacity demand, backup capacity demand, and hydrogen production priority coefficient; The fuzzy adjustment module is used to establish the fuzzy control rules of the fuzzy adaptive PID controller for secondary optimization, that is, to adjust the flow rate and gas circulation rate of the thermal management system in real time according to the electrolysis voltage deviation; The safety constraint module is used to establish a dynamic safety constraint mechanism. The dynamic safety constraint mechanism is activated to adjust the heat flux gradient adjustment, gas circulation rate, and temperature. is the curvature safety threshold, is the voltage of the solid oxide electrolytic cell, is the current running time; The efficiency evaluation module is used to extract the grid interaction power, hydrogen production power equivalent, and system abnormal status indicator at each time, and define the system efficiency optimization index to evaluate the system operation optimization effect.

[0015] The algorithm involved in the present invention can be executed by an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the above algorithm calculation is implemented by executing the software by the processor.

[0016] The beneficial effects of the present invention are as follows: the present invention establishes a dual-time scale dynamic model of the electrolytic hydrogen production system, based on the thermal inertia time constant and the gas diffusion time constant. The model order is determined by the criterion of the model, which breaks through the limitations of traditional single-time-scale modeling and enables the accurate characterization of the thermal-electric coupled dynamic process of the solid oxide electrolysis hydrogen production system at multiple time scales, thereby realizing a system dynamic modeling that is more in line with the actual working conditions and reflecting the changes in physical phenomena at different time scales in the system. It effectively solves the problem of accurate characterization of the thermal-electrical coupled dynamics of the solid oxide electrolysis hydrogen production system in the existing technology.

[0017] The present invention coordinates the economic benefits and stability performance of the system under different operating conditions by constructing a dynamic trajectory optimization functional, thereby achieving the goal of improving the economic efficiency of power grid auxiliary services while ensuring the stable operation of the system.

[0018] The present invention designs a fuzzy adaptive PID controller to effectively decouple and coordinately regulate the strongly coupled parameters, thereby accurately coordinating the relationship between the parameters of various physical fields and ensuring the safe and stable operation of the system, thereby achieving reliable decoupling and stable control of multiple physical field parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the process of the present invention; Figure 2 Schematic diagram of the modules of the system of the present invention. DETAILED DESCRIPTION

[0020] The specific implementation modes of the present invention are described in detail below in conjunction with the accompanying drawings.

[0021] Example 1: Figure 1 As shown, a solid oxide electrolysis hydrogen production system operation optimization control method comprises the following steps: Step 1: Establish a dual-time scale dynamic model of the hydrogen production system by electrolysis and output the thermal-electric coupled dynamic process of the solid oxide hydrogen production system by electrolysis.

[0022] The method for establishing a dual-time scale dynamic model of the electrolytic hydrogen production system is: Decomposing PV power fluctuations into high-frequency components and low frequency components , according to the thermal inertia time constant and the current response time constant Determine the order of the dual time scale dynamic model, where ; The dual time scale dynamic model is expressed as: ; in, is the average temperature of the electrolytic cell, is the heat loss power, is the system heat capacity, is the temperature self-regulation coefficient, is the target temperature setting value of the electrolytic cell, is the electrolysis current, is the system equivalent resistance, is the current reference value, Current running time .

[0023] ,in, is the density of the electrolytic cell material , is the specific heat , is the volume of the electrolytic cell , is the convective heat transfer coefficient , is the effective heat exchange area .

[0024] ,in, is the electrode effective diffusion coefficient , is a parameter that describes the diffusion capacity of ions or electrons in electrode materials. is the electrode thickness ; Step 2: Based on the dual-time scale dynamic model established in step 1, the actual electrolysis efficiency, cumulative heat load, and grid interaction power at each time are extracted from the thermal-electric coupling dynamic process, and the dynamic trajectory optimization functional is constructed and the optimal control is solved. The dynamic trajectory optimization functional is defined as: .

[0025] Among them, G is the operating performance index of the solid oxide electrolysis hydrogen production system, is the end time of the integration, that is, the end time of the system optimization control process, is the starting time of integration, that is, the initial moment when the system starts the optimization control process. is the actual electrolysis efficiency, is the benchmark efficiency setting value, Cumulative heat load , Grid interaction power , is the power constraint threshold, is the positive projection operator.

[0026] , , is the time-varying weight coefficient, and the calculation formula is: .

[0027] in, is the efficiency tracking gain factor (a parameter used to adjust and optimize the system efficiency tracking performance, which can be set according to actual needs, and the same applies to other factors), e is a natural constant, is the power regulation factor, Real-time photovoltaic power , is the minimum operating power threshold, Dead zone width for power regulation , The grid interaction adjustment factor (a parameter used to adjust and optimize the interaction power between the solid oxide electrolysis hydrogen production system and the grid) is discretized by the Legendre pseudo-spectral method to solve the functional and obtain the optimal operation trajectory, providing a target reference for the operation of the solid oxide electrolysis hydrogen production system. Step 3: Combining the dual-time scale dynamic model of step 1 and the optimal operation trajectory obtained in step 2, after achieving optimal control, a three-dimensional decision space for grid auxiliary services is established for primary optimization to generate demand decisions, including: frequency regulation capacity demand, backup capacity demand, and hydrogen production priority coefficient. The three-dimensional decision space is defined as: .

[0028] in, FM capacity requirements , is the grid frequency regulation demand coefficient, The value range is [0.8,1.2], reflecting the frequency modulation service intensity requirements. is the standard deviation of power fluctuation on a 10-minute scale, Spare capacity requirement , is the reserve capacity ratio coefficient, The value range is [0.5,1.0], representing the compensation ratio of spare capacity. The average power of the sliding window in the previous hour , is the hydrogen production priority coefficient, is the hydrogen tank capacity deviation , Reference value for hydrogen storage tank capacity . The photovoltaic power standard deviation calculated using a sliding time window reflects the intensity of power fluctuations: ; Where N is the number of sampling points within 10 minutes, j is the sampling point number, is the photovoltaic power at sampling point j, is the average power during the window period.

[0029] when >0 (excess hydrogen storage), Approaching 0, lowering the priority of hydrogen production.

[0030] when <0 (insufficient hydrogen storage), Approaching 1, the priority of hydrogen production is increased.

[0031] The grid ancillary service strategy is generated based on this three-dimensional decision space through mixed integer programming, so that the system can take into account its own operating efficiency while participating in grid services; Step 4: Based on the dual-time-scale dynamic model of step 1, the optimal operation trajectory of step 2, and the grid auxiliary service strategy of step 3, the fuzzy control rules of the fuzzy adaptive PID controller are established for secondary optimization, that is, according to the electrolysis voltage deviation Real-time regulation of thermal management system flow and gas circulation rate , the fuzzy control rules are: .

[0032] in, is the proportional gain coefficient, is the steady-state proportionality coefficient, is the transient proportional coefficient, is the voltage fuzzy transition threshold , is the maximum allowable voltage deviation, is a saturation function, is a symbolic function.

[0033] The gas circulation rate is regulated using a differential tracking algorithm: .

[0034] in, is the gas circulation rate at time t (current running time), Base cycle rate , is the differential compensation gain coefficient, is the voltage of the solid oxide electrolytic cell (SOEC) , is the voltage change acceleration , in order to achieve precise control of the system operation, d is the differential symbol, represents the second-order derivative, that is, the second derivative with respect to t; Step 5: Establish a dynamic safety constraint mechanism to ensure system operation safety: During the entire system operation, when The dynamic safety constraint mechanism is activated to make corresponding adjustments, such as the heat flux gradient adjustment amount, gas circulation rate, and temperature. is the curvature safety threshold ,When this inequality is satisfied, it means that the system voltage changes too drastically, which may cause damage to the equipment, and it is necessary to trigger the dynamic safety constraint mechanism to adjust the system parameters.

[0035] The above corresponding adjustments include: Heat flux gradient adjustment , is the symbol of partial derivative; The gas circulation rate is adjusted to , is the safety attenuation factor , is the time interval elapsed during the run; Limiting the temperature , is the temperature coupling coefficient , is the target temperature, is the minimum temperature, is the maximum temperature, It is the photovoltaic power difference before and after operation to ensure the stable operation of the system under various conditions.

[0036] Step 6: Extract the grid interaction power, hydrogen production power equivalent, and system abnormal status indicator at each time, and define the system efficiency optimization index to evaluate the system operation optimization effect. System efficiency optimization index for: .

[0037] in, is the hydrogen production power equivalent , is the efficiency loss factor corresponding to the i-th abnormal state, is the system abnormal state indicator, i is the number of abnormal states, i∈{1,2,3} corresponds to three abnormal states: electrolytic cell overheating, insufficient gas purity, and voltage instability. This indicator is used to evaluate the efficiency of the entire optimization control method and provide a basis for further optimization.

[0038] Example 2: Figure 2 As shown, a solid oxide electrolysis hydrogen production system operation optimization control system is used to implement the method in Example 1, comprising: Model building module, used to build a dual-time scale dynamic model of the electrolytic hydrogen production system and output the thermal-electric coupled dynamic process of the solid oxide electrolytic hydrogen production system; The operation trajectory optimization module is used to extract the actual electrolysis efficiency, cumulative heat load, and grid interaction power at each time from the thermal-electric coupling dynamic process, and accordingly construct a dynamic trajectory optimization functional and solve the optimal control; The service strategy generation module is used to establish a three-dimensional decision space for power grid auxiliary services for primary optimization after achieving optimal control, and generate demand decisions, including: frequency regulation capacity demand, backup capacity demand, and hydrogen production priority coefficient; The fuzzy adjustment module is used to establish the fuzzy control rules of the fuzzy adaptive PID controller for secondary optimization, that is, to adjust the flow rate and gas circulation rate of the thermal management system in real time according to the electrolysis voltage deviation; The safety constraint module is used to establish a dynamic safety constraint mechanism. The dynamic safety constraint mechanism is activated to adjust the heat flux gradient adjustment amount, gas circulation rate and temperature; The efficiency evaluation module is used to extract the grid interaction power, hydrogen production power equivalent, and system abnormal status indicator at each time, and define the system efficiency optimization index to evaluate the system operation optimization effect.

Claims

1. A solid oxide electrolysis hydrogen production system operation optimization control method, characterized in that: The following steps are involved: Step 1: Establish a dual-time scale dynamic model of the electrolytic hydrogen production system and output the thermal-electric coupled dynamic process of the solid oxide electrolytic hydrogen production system; Step 2: Extract the actual electrolysis efficiency, cumulative heat load, and grid interaction power at each time from the thermal-electric coupling dynamic process, and construct a dynamic trajectory optimization functional to solve the optimal control; Step 3: After achieving optimal control, establish a three-dimensional decision space for grid auxiliary services to perform primary optimization and generate demand decisions, including: frequency regulation capacity demand, backup capacity demand, and hydrogen production priority coefficient; Step 4, establishing the fuzzy control rules of the fuzzy adaptive PID controller for secondary optimization, that is, adjusting the flow rate and gas circulation rate of the thermal management system in real time according to the electrolysis voltage deviation; Step 5: Establish a dynamic security constraint mechanism. The dynamic safety constraint mechanism is activated to adjust the heat flux gradient adjustment, gas circulation rate, and temperature. is the curvature safety threshold, is the voltage of the solid oxide electrolytic cell, is the current running time; Step 6: Extract the grid interaction power, hydrogen production power equivalent, and system abnormal status indicator at each time, and define the system efficiency optimization index to evaluate the system operation optimization effect.

2. A solid oxide electrolysis hydrogen production system operation optimization control method as claimed in claim 1, characterized in that: In step 1, the method for establishing a dual-time scale dynamic model of the electrolytic hydrogen production system is: Decomposing PV power fluctuations into high-frequency components and low frequency components , according to the thermal inertia time constant and the current response time constant Determine the order of the dual time scale dynamic model, where ; The dual time scale dynamic model is expressed as: ； in, is the average temperature of the electrolytic cell, is the heat loss power, is the system heat capacity, is the temperature self-regulation coefficient, is the target temperature setting value of the electrolytic cell, is the electrolysis current, is the system equivalent resistance, is the current reference value; ,in, is the density of the electrolytic cell material, is the specific heat capacity, is the volume of the electrolytic cell, is the convective heat transfer coefficient, is the effective heat exchange area; ,in, is the electrode effective diffusion coefficient, is the electrode thickness.

3. The method for optimizing the operation of a solid oxide electrolysis hydrogen production system according to claim 1, characterized in that: In step 2, the dynamic trajectory optimization functional is defined as: ； Among them, G is the operating performance index of the solid oxide electrolysis hydrogen production system, is the end time of integration, is the starting time of integration, is the actual electrolysis efficiency, is the benchmark efficiency setting value, is the cumulative heat load, is the grid interaction power, is the power constraint threshold, is the orthographic projection operator, , , is the time-varying weight coefficient.

4. A solid oxide electrolysis hydrogen production system operation optimization control method as claimed in claim 3, characterized in that: The calculation formula of the time-varying weight coefficient is: ； in, is the efficiency tracking gain factor, e is a natural constant, is the thermal inertia time constant, is the power regulation factor, is the real-time photovoltaic power, is the minimum operating power threshold, is the power regulation dead zone width, is the grid interaction adjustment factor.

5. The method for optimizing the operation of a solid oxide electrolysis hydrogen production system according to claim 1, characterized in that: In step 3, the three-dimensional decision space is defined as: ； in, For frequency modulation capacity requirements, is the grid frequency regulation demand coefficient, is the standard deviation of power fluctuation on a 10-minute scale, For spare capacity requirements, is the reserve capacity ratio coefficient, is the average power of the sliding window in the previous hour, is the hydrogen production priority coefficient, is the hydrogen storage tank capacity deviation, is the reference value of hydrogen storage tank capacity, e is a natural constant, is the grid interaction power.

6. A solid oxide electrolysis hydrogen production system operation optimization control method as claimed in claim 1, characterized in that: In step 4, the fuzzy control rules of the fuzzy adaptive PID controller are designed and the adjustment parameters are specifically as follows: Real-time regulation of thermal management system flow and gas circulation rate , the fuzzy control rules are: ； in, is the proportional gain coefficient, is the steady-state proportionality coefficient, is the transient proportional coefficient, is the voltage fuzzy transition threshold, is the maximum allowable voltage deviation, is the saturation function, is a symbolic function.

7. A solid oxide electrolysis hydrogen production system operation optimization control method as claimed in claim 6, characterized in that: The gas circulation rate The adjustment algorithm is: ； in, is the gas circulation rate at time t, is the base cycle rate, is the differential compensation gain coefficient, is the voltage change acceleration.

8. A solid oxide electrolysis hydrogen production system operation optimization control method as claimed in claim 6, characterized in that: In step 5, establishing a dynamic safety constraint mechanism to ensure system operation safety specifically refers to: during the entire system operation process, when there is Adjust parameters when Parameter adjustments include: Heat flux gradient adjustment The calculation formula is: ； The gas circulation rate is adjusted to the new gas circulation rate , the new gas circulation rate calculation formula is: ； in, is the safety attenuation coefficient, e is a natural constant, is the time interval elapsed during the run; Limiting the temperature , is the temperature coupling coefficient, is the target temperature, is the minimum temperature, is the maximum temperature, is the photovoltaic power difference before and after operation.

9. A solid oxide electrolysis hydrogen production system operation optimization control method as claimed in claim 1, characterized in that: In step 6, the system efficiency optimization index for: ； in, is the hydrogen production power equivalent, is the efficiency loss factor corresponding to the i-th abnormal state, is the system abnormal state indicator, i is the number of abnormal states, i∈{1,2,3}, is the grid interaction power, is the real-time photovoltaic power.

10. A solid oxide electrolysis hydrogen production system operation optimization control system, used to implement the method according to any one of claims 1 to 9, characterized in that: include: Model building module, used to build a dual-time scale dynamic model of the electrolytic hydrogen production system and output the thermal-electric coupled dynamic process of the solid oxide electrolytic hydrogen production system; The operation trajectory optimization module is used to extract the actual electrolysis efficiency, cumulative heat load, and grid interaction power at each time from the thermal-electric coupling dynamic process, and accordingly construct a dynamic trajectory optimization functional and solve the optimal control; The service strategy generation module is used to establish a three-dimensional decision space for power grid auxiliary services for primary optimization after achieving optimal control, and generate demand decisions, including: frequency regulation capacity demand, backup capacity demand, and hydrogen production priority coefficient; The fuzzy adjustment module is used to establish the fuzzy control rules of the fuzzy adaptive PID controller for secondary optimization, that is, to adjust the flow rate and gas circulation rate of the thermal management system in real time according to the electrolysis voltage deviation; The safety constraint module is used to establish a dynamic safety constraint mechanism. The dynamic safety constraint mechanism is activated to adjust the heat flux gradient adjustment, gas circulation rate, and temperature. is the curvature safety threshold, is the voltage of the solid oxide electrolytic cell, is the current running time; The efficiency evaluation module is used to extract the grid interaction power, hydrogen production power equivalent, and system abnormal status indicator at each time, and define the system efficiency optimization index to evaluate the system operation optimization effect.

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