SOEC physical field response prediction method under fluctuation renewable energy source input

Abstract

The invention discloses an SOEC physical field response prediction method under fluctuating renewable energy source input, and relates to the technical field of electrolytic cells. According to the method, a computational domain is established according to a single flow channel of the SOEC, and the fluctuation voltage corresponding to the fluctuation renewable energy source is determined according to a power time fluctuation change curve of the fluctuation renewable energy source, so that the fluctuation voltage is used as the working voltage of the SOEC to determine the boundary condition of numerical calculation in SOEC transient model construction. A model equation set of an SOEC transient model is established based on control equations such as a coupled mass conservation equation, an energy conservation equation, a momentum conservation equation, a gas multi-component mass fraction equation, an electron potential conservation equation and an ion potential conservation equation; the response characteristics of the SOEC in the component field and the temperature field under the fluctuation renewable energy input are predicted, and the prediction accuracy of the dynamic response of the SOEC physical field under the fluctuation renewable energy input is improved.

Description

Technical Field

[0001] This invention relates to the field of electrolyzer technology, and in particular to a method for predicting the physical field response of SOEC under fluctuating renewable energy input. Background Technology

[0002] Currently, renewable energy sources (such as wind and solar power) are characterized by their cleanliness, abundant resources, and high volatility, and are often used as energy inputs in conjunction with electrolyzers for hydrogen production and storage. High-temperature solid oxide electrolyzers (SOECs) have attracted widespread attention due to their high efficiency, wide fuel compatibility, and low cost. A unique feature of SOEC technology is its ability to co-electrolyze water and carbon dioxide, producing syngas while simultaneously capturing carbon. To address the volatility of renewable energy sources and implement effective control strategies, transient modeling of SOECs is typically required to determine the multiphysics response characteristics within the electrolyzer.

[0003] In existing technologies, SOEC transient simulation is mainly combined with step inputs (such as rapid changes in feed flow rate) or mode switching (switching between fuel cell mode and electrolysis mode) to determine the response characteristics of multiphysics fields in the electrolyzer when step inputs or mode switching occur. However, it fails to take into account the impact of short-term continuous fluctuations of renewable energy on the response characteristics of the electrolyzer's physical fields, resulting in low prediction accuracy. Summary of the Invention

[0004] Therefore, it is necessary to provide a method for predicting the physical field response of SOEC under fluctuating renewable energy input to address the above-mentioned technical problems.

[0005] The present invention adopts the following technical solution: This invention provides a method for predicting the physical field response of a SOEC (Solar Energy Controller) under fluctuating renewable energy input, wherein the SOEC includes an SOEC stack, and the SOEC stack includes multiple SOEC cells. The method includes: Determine the fluctuating voltage corresponding to the fluctuating renewable energy source based on the power-time fluctuation curve of the fluctuating renewable energy source; Using a single SOEC cell and a single flow channel in an SOEC stack as the computational domain, the fluctuating voltage is used as the working voltage for numerical calculation of the SOEC transient model to construct the boundary conditions for numerical calculation. Multiple control equations in the co-electrolysis are coupled to obtain the model equation set of the SOEC transient model. By numerically solving the model equations, the dynamic responses of the SOEC component field and temperature field under fluctuating renewable energy input were determined. Among them, various governing equations include the mass conservation equation, momentum conservation equation, energy conservation equation, gas multi-component mass fraction equation, electronic potential conservation equation, and ion potential conservation equation.

[0006] Optionally, the SOEC battery includes an anode bipolar plate, an anode channel, an anode diffusion layer, an anode catalyst layer, an electrolyte, a cathode catalyst layer, a cathode diffusion layer, a cathode channel, and a cathode bipolar plate; The computational domain includes: an anode bipolar plate, an anode channel, an anode diffusion layer, an anode catalyst layer, an electrolyte, a cathode catalyst layer, a cathode diffusion layer, a cathode channel, and a cathode bipolar plate.

[0007] Optionally, coupling the gas multi-component mass fraction equation specifically includes: The following equation is used to express the gas multi-component mass fraction equation in single-channel co-electrolysis based on molecular free diffusion coupling, and the gas multi-component mass fraction equation in porous SOEC based on molecular free diffusion and Knudsen diffusion coupling co-electrolysis: , ; in, For time, Porosity of porous media For the tortuosity of porous media, For apparent density, Components The mass fraction, For apparent speed, For Hamiltonian operators, For the effective diffusion coefficient, For the effective binary diffusion coefficient, The effective Knudsen diffusion coefficient.

[0008] Optionally, the step of using the fluctuating voltage as the operating voltage for the numerical calculation of the SOEC transient model to construct the boundary conditions for the numerical calculation specifically includes: The fluctuating voltage is used as the operating voltage for numerical calculations of the SOEC transient model. The boundary conditions for numerical calculation are formed by setting the outer boundary of the computational domain as an adiabatic boundary, setting the inlet of the anode and cathode channels as a mass flow inlet, setting the working pressure as a constant pressure, setting the boundary at the end face of the SOEC cell anode bipolar plate as the working voltage, and setting the boundary at the end face of the SOEC cell cathode bipolar plate as a reference potential of zero.

[0009] Optionally, the method further includes: The single SOEC cell in the SOEC stack is divided into two parts based on the middle of the anode and cathode, and a symmetrical boundary is set at the dividing point. The part after the division is then meshed. The numerical solution of the model equations to determine the dynamic responses of the SOEC component field and temperature field under fluctuating renewable energy input specifically includes: The model equations are numerically solved based on the meshed portion to obtain the dynamic response of the SOEC component field and temperature field under fluctuating renewable energy input in the meshed portion. Furthermore, the dynamic response of the SOEC component field and temperature field under fluctuating renewable energy input in the unmeshed portion is obtained through symmetry based on the symmetric boundary.

[0010] This invention provides a device for predicting the physical field response of an SOEC (Solar Energy Controller) under fluctuating renewable energy input. The SOEC includes an SOEC stack, which in turn includes multiple SOEC cells. The device comprises: The voltage determination module is used to determine the fluctuating voltage corresponding to the fluctuating renewable energy source based on the power-time fluctuation curve of the fluctuating renewable energy source. The modeling module is used to construct the boundary conditions for numerical calculation of SOEC transient model by taking the single channel of a single SOEC cell in SOEC stack as the computational domain and using the fluctuating voltage as the working voltage for numerical calculation of SOEC transient model. It couples multiple control equations in co-electrolysis to obtain the model equation set of SOEC transient model. The prediction module is used to determine the dynamic response of the SOEC component field and temperature field under fluctuating renewable energy input by numerically solving the model equations. Among them, various governing equations include the mass conservation equation, momentum conservation equation, energy conservation equation, gas multi-component mass fraction equation, electronic potential conservation equation, and ion potential conservation equation.

[0011] The present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for predicting the physical field response of SOEC under fluctuating renewable energy input.

[0012] The present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the above-described method for predicting the physical field response of SOEC under fluctuating renewable energy input.

[0013] The above-mentioned at least one technical solution adopted in this invention can achieve the following beneficial effects: This invention establishes a computational domain based on the single-channel SOEC and determines the fluctuating voltage corresponding to the fluctuating renewable energy source based on the power-time fluctuation curve of the fluctuating renewable energy source. This fluctuating voltage is then used as the operating voltage of the SOEC to determine the boundary conditions for numerical calculations in the construction of the SOEC transient model. Based on this, a set of model equations for the SOEC transient model is established using multiple governing equations coupled in the co-electrolysis. Through numerical solution, the response characteristics of the SOEC component field and temperature field under fluctuating renewable energy input are predicted, improving the accuracy of predicting the dynamic response of the SOEC physical field under fluctuating renewable energy input. Attached Figure Description

[0014] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings:

[0015] Figure 1 A schematic diagram of the process for predicting the physical field response of SOEC under fluctuating renewable energy input provided by this invention; Figure 2 This invention provides a schematic diagram illustrating the variation of fluctuating voltage input over time. Figure 3a This invention provides a schematic diagram of a three-dimensional model of the computational domain of an electrolytic cell. Figure 3b A schematic diagram of a two-dimensional cross-sectional geometry of the computational domain of an electrolytic cell provided by the present invention. Figure 4 This invention provides a schematic cloud map showing the changes in the molar fractions of hydrogen and carbon monoxide and the temperature over time in the cathode flow channel, cathode diffusion layer and cathode catalyst layer, calculated using a simulation method. Figure 5 A schematic diagram of the calculated mole fraction of oxygen in the anode flow channel, anode diffusion layer, and anode catalyst layer, and the corresponding temperature changes over time, provided by the present invention. Figure 6 A schematic diagram of an SOEC physics field response prediction device under fluctuating renewable energy input provided by the present invention; Figure 7 This is a schematic diagram of a computer device for implementing a method for predicting the physical field response of SOEC under fluctuating renewable energy input, as provided by the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0017] Generally, system coupling modeling that combines SOEC with renewable energy involves renewable energy power generation modeling and SOEC transient modeling. Taking wind energy as an example, it involves zero-dimensional wind power generation modeling and three-dimensional SOEC transient modeling.

[0018] In existing models, transient simulations of SOEC are mainly combined with solar energy fluctuations (intermittent power supply from solar energy fluctuations may lead to mode switching), focusing on the dynamic response of each physical field when the electric field jumps instantaneously, or combined with long-term wind energy input, mainly focusing on the dynamic response of the electric field and temperature field. However, there is a lack of attention to the distribution of each physical field in SOEC when the load fluctuates continuously for a short time. For example, there is a lack of comparative studies on the dynamic response of the component fields and temperature field when the electric field changes continuously for short-term oscillating wind energy input.

[0019] Based on this, the present invention provides a method for constructing a transient model of SOEC under fluctuating renewable energy input, and compares the delayed response characteristics of the component field and temperature field in SOEC. The fluctuating renewable energy can be various renewable energy sources such as wind power and solar power; in one or more embodiments of the present invention, wind power is used as an example for illustration.

[0020] For short-term fluctuating wind energy input, the power fluctuation curve of wind energy over time can be converted into a fluctuating voltage applied to the SOEC stack. A calculation domain is established by taking a single cell and a single flow channel in the SOEC stack. The calculation domain includes the anode bipolar plate (ABP), anode flow channel, anode diffusion layer (AGDL), anode catalyst layer (ACL), electrolyte, cathode catalyst layer (CCL), cathode diffusion layer (CGDL), cathode flow channel, and cathode bipolar plate (CBP).

[0021] The technical solutions provided by the various embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0022] Figure 1 This is a schematic diagram of a method for predicting the physical field response of SOEC under fluctuating renewable energy input according to the present invention, which specifically includes the following steps: S101: Determine the fluctuating voltage corresponding to the fluctuating renewable energy source based on the power time fluctuation curve of the fluctuating renewable energy source.

[0023] S102: Using a single SOEC cell and a single flow channel in an SOEC stack as the computational domain, the fluctuating voltage is used as the working voltage for numerical calculation of the SOEC transient model to construct the boundary conditions for numerical calculation. Multiple control equations in the co-electrolysis are coupled to obtain the model equation set of the SOEC transient model.

[0024] S103: By numerically solving the model equations, the dynamic responses of the SOEC component field and temperature field under fluctuating renewable energy input are determined.

[0025] For ease of explanation, the following description focuses solely on the server as the executing entity. The server mentioned in this invention can be a server set up on a business platform, or a device such as a desktop computer or laptop computer capable of executing the solution of this invention.

[0026] Generally, when predicting the physical field response of SOEC, the server can first model the SOEC transient model, and then perform numerical calculations based on the computational domain and boundary conditions to obtain the dynamic response of each physical field of SOEC.

[0027] In one or more embodiments of the present invention, SOEC includes an SOEC stack, which typically includes multiple SOEC cells. Each SOEC cell may include components such as an anode bipolar plate, an anode channel, an anode diffusion layer, an anode catalyst layer, an electrolyte, a cathode catalyst layer, a cathode diffusion layer, a cathode channel, and a cathode bipolar plate.

[0028] Therefore, the calculation region can be determined as a single flow channel in an SOEC single cell, including the anode bipolar plate (ABP), anode flow channel, anode diffusion layer (AGDL), anode catalyst layer (ACL), electrolyte, cathode catalyst layer (CCL), cathode diffusion layer (CGDL), cathode flow channel, and cathode bipolar plate (CBP).

[0029] For boundary conditions, the fluctuation voltage corresponding to the fluctuation renewable energy can be determined based on the power-time fluctuation curve of the fluctuation renewable energy. The fluctuation voltage is used as the working voltage for numerical calculation of the SOEC transient model, and further boundary conditions are formed. Specifically, the boundary outside the calculation domain can be set as an adiabatic boundary, the inlet of the anode and cathode channels can be set as a mass flow inlet, the working pressure can be set as a constant pressure, the boundary at the end face of the SOEC anode bipolar plate can be set as the working voltage of SOEC, and the boundary at the end face of the SOEC cathode bipolar plate can be set as the reference potential zero, thus forming the boundary conditions for numerical calculation.

[0030] Subsequently, based on this, multiple governing equations in co-electrolysis can be coupled to obtain the model equation set of the SOEC transient model. Specifically, multiple governing equations can include mass conservation equation, momentum conservation equation, energy conservation equation, gas multi-component mass fraction equation, electronic potential conservation equation, and ion potential conservation equation.

[0031] The mass fraction equation for the mixed gas can be expressed as: In the formula, t For time, It is a component The mass fraction, It is a component Quality source items, It is the effective diffusion coefficient. It is the apparent speed. It refers to the porosity of porous media. It is apparent density.

[0032] The mass and momentum conservation equations for multi-gas components include: mass conservation equation: Momentum conservation equation: In the formula, It's pressure. It is dynamic viscosity. and These represent the mass source term and the momentum source term, respectively.

[0033] The electrochemical model equations for SOEC include: The electron potential equation is: The equation for ionic potential is: In the formula, where and These represent effective electronic conductivity and effective ionic conductivity, respectively. and These are the source terms for electronic potential and ion potential, respectively.

[0034] Energy conservation equation: In the formula, For specific heat capacity, For effective thermal conductivity, For heat source items, This refers to the operating temperature.

[0035] The source terms of the electronic potential equation: The source term of the ion potential equation: in, The electrochemical reaction rate of the anode catalyst layer. The electrochemical reaction rate of the cathode catalyst layer is given. The electrochemical reaction rates of the anode and cathode catalyst layers are:

[0036] In the formula, The electrochemical reaction rate of H2O in the cathode catalyst layer. The exchange current density of H2O at the cathode. This represents the electrochemical reaction rate of CO2 in the cathode catalyst layer. The exchange current density of CO2 at the cathode. and The charge transfer coefficient, F Faraday constant , R Universal gas constant , This refers to the number of electrons generated or consumed during the electrochemical process. and These are the ratios of the active surface area of ​​the cathode / electrode electrochemical reaction to the volume of the cathode / electrode catalyst layer, respectively. It is the anode exchange current density. and These are the activation overpotentials of the cathode and anode, respectively.

[0037] Taking a system coupled with an SOEC fuel cell stack and a wind turbine as an example, the wind turbine converts fluctuating wind energy into electrical energy, which is then applied to the SOEC fuel cell stack for the co-electrolysis of H2O and CO2. The SOEC fuel cell stack consists of multiple SOEC single cells connected in series. The computational domain can be a single flow channel within the SOEC single cell. In one or more embodiments of this invention, to reduce computational load and improve computational efficiency, before coupling and solving the various governing equations, the single flow channel of a single SOEC cell in the SOEC fuel cell stack can be symmetrically divided into two parts along the flow direction between the anode and cathode. A symmetrical boundary is set at the bisector, and one of the bisectors is then meshed. This halves the number of meshes, reducing the computational load for subsequent SOEC transient modeling.

[0038] Specifically, when numerically solving the model equations to determine the dynamic responses of the SOEC component field and temperature field under fluctuating renewable energy input, the model equations can be numerically solved based on the meshed portion to obtain the dynamic responses of the SOEC component field and temperature field under fluctuating renewable energy input in the meshed portion. Furthermore, by using symmetry based on symmetric boundaries, the dynamic responses of the SOEC component field and temperature field under fluctuating renewable energy input in the unmeshed portion can be obtained. It is evident that the entire process reduces the computational load by half.

[0039] When the electrode material is a porous medium with a pore size in the micrometer range, the diffusion of gas in the porous medium mainly includes two cases: (1) when the pore size of the porous medium is much larger than the mean free path of the molecules, the molecules in the porous medium mainly diffuse freely; (2) when the porous medium material is smaller than the mean free path of the molecules, the molecules in the porous medium are prone to collide with the solid material, and Knudsen diffusion is the main process. The channel size is much larger than the mean free path of the molecules, so only the free diffusion of molecules is considered in the channel; the size of the porous electrode is similar to the mean free path of the molecules, so both diffusion mechanisms need to be considered. In this embodiment, it is assumed that all gases are ideal gases, and the extended Fick model is used to comprehensively consider the influence of the two diffusion mechanisms. The equation for the conservation of the mass fraction of the mixed gas can be expressed as:

[0040] In the formula, The effective diffusion coefficient can be expressed as: in and These are the effective binary diffusion coefficient and the effective Knudsen diffusion coefficient, respectively. Porosity of porous media The tortuosity of porous media.

[0041] The effective binary diffusion coefficient and the effective Knudsen diffusion coefficient can be determined by the following formula: In the formula, It is the average aperture size. It is a component molecular weight, It is the diffusion volume of a specific atom.

[0042] The reaction in SOEC is relatively complex, involving complex heat transfer processes and heat source terms. Convective heat transfer exists between the flow channels and the plates, heat conduction exists between the solid media, and radiative heat transfer also occurs between the SOEC solids and with the environment. Since radiative heat transfer is relatively small, it is neglected in this embodiment. The energy conservation equation is:

[0043] Electrochemical reactions occur in the anode and cathode catalyst layers of SOEC. The electrochemical reaction process is as follows: H2O and CO2 at the cathode gain electrons to generate H2 and CO, and... , The electrons pass through the solid electrolyte to the anode, where they lose electrons to generate oxygen. Electrons are conducted in solid materials other than the electrolyte and in the external circuitry, while ions are conducted in the cathode catalyst layer, the anode catalyst layer, and the solid electrolyte.

[0044] The electron conservation equation is: The ion conservation equation is: in and Let represent the effective electronic conductivity and effective ionic conductivity, respectively. These can be expressed as the Brugmann correction for the conductivity of purely conductive electronic and purely conductive ionic particles, related to the porosity and tortuosity of the porous electrode: For electronic conductivity, denoted as ionic conductivity.

[0045] and These are the source terms for electronic potential and ion potential, respectively, which can be calculated using the Butler-Volmer equation: and This refers to the cathode / anode exchange current density, which is mainly affected by the partial pressure of the reactant gas components, catalyst activity, and electrolytic cell temperature. In the formula, , and Both are reaction rate constants, and P is the partial pressure of the gas at the three-phase interface. The partial pressure of water vapor. Standard atmospheric reference pressure, Let be the partial pressure of carbon dioxide, and m be the proportionality constant of the partial pressure. It is the activation energy of the reaction. The activation energy of the cathode reaction. This is the activation energy for the anodic reaction. and The activation overpotential of the cathode / anode can be calculated using the following formula:

[0046] and These are the electronic potential and ionic potential, respectively, and the reversible potential. The calculation formula is as follows: In the formula, The reversible potential of water, For non-standard generation Gibbs free energy, This is the partial pressure of hydrogen gas. This is the partial pressure of oxygen. For reference pressure, This represents the reversible potential of carbon dioxide. This is the partial pressure of carbon monoxide.

[0047] In the formula for calculating the reversible potential, the partial pressure of each gas component is the partial pressure at the cross-section of the gas diffusion layer and the catalyst layer. Therefore, the concentration loss caused by the transport of gas components in the porous medium inside the electrolytic cell is already implied in the calculation process of the reversible potential.

[0048] The ohmic overpotential caused by electron and ion transport can be calculated using Ohm's law: In the formula, For Ohm overpotential, For current density, For transmission length, is the electrical conductivity.

[0049] The output voltage of an actual electrolytic cell can be obtained by adding various overpotentials to the open-circuit voltage: In the formula, This refers to the output voltage of the electrolytic cell. Open circuit voltage, For cathode activation overpotential, This is the anodic activation overpotential.

[0050] Chemical catalytic reactions occur in the cathode diffusion layer of the SOEC, with the dominant reactions including reversible methane steam reforming (MSR) and reversible water vapor transfer (WGSR). For MSR, the catalytic activity of the anode nickel particles at the high operating temperature of the SOEC allows them to act as a catalyst, thus the MSR reaction mainly occurs on the surface of the anode nickel particles. For WGSR, no additional catalyst is required. The reaction rate model used in this embodiment is as follows: In the formula, and These represent the chemical reaction rates of the methane steam reforming reaction and the water-gas reaction, respectively. For methane gas partial pressure, / It is the rate constant of the forward chemical reaction. / Let be the equilibrium constant for a chemical reaction, and its calculation expression is as follows: The amount of heat generated or absorbed in a chemical reaction is calculated using the enthalpy changes of the reaction products and reactants. For the forward MSR reaction, it is a strongly endothermic reaction; for the forward WGSR reaction, it is an exothermic reaction. The formula for calculating the heat of reaction is as follows: The model source terms in the above equations are shown below: quality: ,unit: ; momentum: ,unit: ; Methane: ,unit: ; water: ,unit: ; hydrogen: ,unit: ; carbon dioxide: ,unit: ; Carbon monoxide: ,unit: ; oxygen: ,unit: ; Calories: ,unit: ; ,unit: ; ,unit: ; ,unit: ; ,unit: ; Electron potential: ,unit: ; Ion potential: ,unit: ; In the formula, Indicates flow channel, Components CH 4 quality source items, Components H 2 quality source items, Components CO Quality source items, Components H 2 O Quality source items, Components CO 2 quality source items, Components O 2. Quality source item. For porous media permeability, Components CH A molecular weight of 4 Components H 2 O molecular weight, for H 2 O The electrochemical reaction rate, Components H The molecular weight of 2 Components H The electrochemical reaction rate of 2, Components CO The molecular weight of 2 Components CO The electrochemical reaction rate of 2, Components CO molecular weight, Components CO The electrochemical reaction rate, Components O The electrochemical reaction rate of 2, Components O The molecular weight of 2. The ion current density, It is the ionic conductivity. For electron current density, For electronic conductivity, This represents the active surface area of ​​the cathode catalyst. The standard reaction entropy for the methane steam reforming reaction is given. The standard reaction entropy of the water-gas reaction. The anode current density, This represents the active surface area of ​​the anode catalyst. This represents the molar entropy change of the anodic reaction.

[0051] All the above equations constitute a three-dimensional full-size model of a solid oxide electrolyzer (SOEC). Through numerical calculation, the distribution and performance of various physical fields within the SOEC can be obtained, i.e., the changes in temperature and mole fraction of each component over time within the computational domain. Subsequently, based on the SOEC performance reflected by temperature and composition, the response caused by fluctuations in renewable energy sources can be reduced by changing the flow rate and increasing active cooling. The boundary conditions corresponding to the above governing equations are set as follows: the boundary outside the computational domain is set as an adiabatic boundary; the inlet of the anode and cathode channels is a mass flow rate inlet; the operating pressure is a constant pressure; the boundary at the end face of the anode bipolar plate is set as the battery operating voltage, i.e., the voltage applied; and the boundary at the end face of the cathode bipolar plate is set as a reference potential. The mass fraction of each gas component at the inlet of the anode and cathode flow channels can be calculated using the following formula:

[0052] In the formula, Represents the mole fraction of a gaseous component. This indicates the molar mass of the gaseous component.

[0053] based on Figure 1The present invention describes a method for predicting the physical field response of SOEC under fluctuating renewable energy input. This method establishes a computational domain based on the single-channel nature of SOEC and determines the fluctuating voltage corresponding to the fluctuating renewable energy based on the power-time fluctuation curve. This fluctuating voltage is then used as the operating voltage of SOEC to determine the boundary conditions for numerical calculations in the construction of the SOEC transient model. Based on this, a set of model equations for the SOEC transient model is established by coupling the mass conservation equation, energy conservation equation, momentum conservation equation, gas multi-component mass fraction equation, electron potential conservation equation, and ion potential conservation equation. Through numerical solution, the method predicts the response characteristics of the component field and temperature field of SOEC under fluctuating renewable energy input, providing theoretical guidance for the dynamic response characteristics of SOEC.

[0054] When applying the SOEC physics response prediction method under fluctuating renewable energy input provided by this invention, it is not necessary to rely on... Figure 1 The steps shown are executed in sequence. The specific execution order of each step can be determined as needed, and this invention does not impose any restrictions on it.

[0055] Furthermore, this invention also provides an embodiment of the SOEC physical field response prediction method of this invention. In this embodiment, the single-channel electrolytic cell has a channel length of 100 mm, a channel width of 2 mm, a ridge width of 1 mm (on one side) for the electrode, a channel height of 1 mm, and an electrode thickness of 1 mm. The thicknesses of the cathode diffusion layer, cathode catalyst layer, electrolyte, anode catalyst layer, and anode diffusion layer are 0.54 mm, 0.01 mm, 0.015 mm, 0.01 mm, and 0.02 mm, respectively. Because the electrolytic cell of this structure is symmetrical, it is divided into two equal parts in the middle during the solution process, and symmetrical boundary conditions are set.

[0056] Assuming the fluctuating voltage input from wind energy is fixed at 1.2V for 0-1s, then linearly decreases from 1.2V to 1.0V for 1-2s, then linearly increases from 1.0V to 1.1V for 2-3s, continues to linearly increase from 1.1V to 1.3V for 3-4s, and then linearly decreases from 1.3V to 0.9V for 4-5s, eventually remaining at 0.9V until the electrolytic cell reaches a stable state. The ambient pressure is 1 atm, and the operating temperature of the electrolytic cell is... The intake temperature of the anode and cathode is also... The cathode fuel gas inlet flow rate is 500 sccm, and the mole fractions of H2O, CO2, and H2 at the cathode inlet are 0.45, 0.45, and 0.1, respectively. The anode air inlet flow rate is 250 sccm, and the mole fractions of O2 and N2 are 0.21 and 0.79, respectively. Based on these parameters, the physical field distribution characteristics of SOEC under fluctuating voltage input are solved, i.e., the changes in temperature and the mole fraction of each component in the computational domain over time.

[0057] Figure 2 This is a schematic diagram illustrating the variation of fluctuating voltage input over time in this invention. Figure 3a This is a schematic diagram of a three-dimensional model and symmetrical boundary of a partial electrolytic cell computational domain in this invention. Figure 3a The example given is the left portion of the symmetry boundary. It is understood that the right portion, which is not shown, is completely symmetrical to the left based on the symmetry boundary. Figure 3b This is a schematic diagram of the two-dimensional cross-sectional geometry of a partial electrolytic cell computational domain in this invention. Figure 4 This is a schematic diagram of the changes in the molar fractions of hydrogen and carbon monoxide and the temperature over time in the cathode flow channel, cathode diffusion layer and cathode catalyst layer, calculated based on a simulation method according to the present invention. Figure 5 This is a schematic diagram of the calculated mole fraction of oxygen in the anode channel, anode diffusion layer, and anode catalyst layer, and the corresponding temperature changes over time, according to the present invention.

[0058] The above describes a method for predicting the physical field response of SOEC under fluctuating renewable energy input, provided by one or more embodiments of the present invention. Based on the same idea, the present invention also provides a corresponding device for predicting the physical field response of SOEC under fluctuating renewable energy input, such as... Figure 6 As shown.

[0059] Figure 6 A schematic diagram of an SOEC physics response prediction device under fluctuating renewable energy input provided by the present invention includes: The voltage determination module 201 is used to determine the fluctuating voltage corresponding to the fluctuating renewable energy based on the power-time fluctuation curve of the fluctuating renewable energy. Modeling module 202 is used to construct the boundary conditions for numerical calculation by taking the single channel of a single SOEC cell in the SOEC stack as the computational domain, using the fluctuating voltage as the working voltage for numerical calculation of the SOEC transient model, and coupling multiple control equations in the co-electrolysis to obtain the model equation set of the SOEC transient model. The prediction module 203 is used to determine the dynamic response of the SOEC component field and temperature field under fluctuating renewable energy input by numerically solving the model equations. Among them, various governing equations include the mass conservation equation, momentum conservation equation, energy conservation equation, gas multi-component mass fraction equation, electronic potential conservation equation, and ion potential conservation equation.

[0060] Specific limitations regarding the SOEC physical field response prediction device under fluctuating renewable energy input can be found in the limitations of the SOEC physical field response prediction method under fluctuating renewable energy input described above, and will not be repeated here. Each module in the aforementioned SOEC physical field response prediction device under fluctuating renewable energy input can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0061] The present invention also provides a computer-readable storage medium storing a computer program that can be used to execute the above-described... Figure 1 A method for predicting the physical field response of SOEC under fluctuating renewable energy inputs is provided.

[0062] The present invention also provides Figure 7 The schematic diagram of the computer device shown is as follows: Figure 7 As shown, at the hardware level, this computer device includes a processor, internal bus, network interface, memory, and non-volatile memory, and may also include other hardware required for business operations. The processor reads the corresponding computer program from the non-volatile memory into memory and then executes it to achieve the above. Figure 1 A method for predicting the physical field response of SOEC under fluctuating renewable energy inputs is provided.

[0063] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0064] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this invention.

Claims

1. A method for predicting the physical field response of SOEC under fluctuating renewable energy input, characterized in that, The SOEC includes an SOEC stack, the SOEC stack includes multiple SOEC cells, and the method includes: Determine the fluctuating voltage corresponding to the fluctuating renewable energy source based on the power-time fluctuation curve of the fluctuating renewable energy source; Using a single SOEC cell and a single flow channel in an SOEC stack as the computational domain, the fluctuating voltage is used as the working voltage for numerical calculation of the SOEC transient model to construct the boundary conditions for numerical calculation. Multiple control equations in the co-electrolysis are coupled to obtain the model equation set of the SOEC transient model. By numerically solving the model equations, the dynamic responses of the SOEC component field and temperature field under fluctuating renewable energy input were determined. Among them, various governing equations include the mass conservation equation, momentum conservation equation, energy conservation equation, gas multi-component mass fraction equation, electronic potential conservation equation, and ion potential conservation equation.

2. The method for predicting the physical field response of SOEC under fluctuating renewable energy input as described in claim 1, characterized in that, The SOEC battery includes an anode bipolar plate, an anode channel, an anode diffusion layer, an anode catalyst layer, an electrolyte, a cathode catalyst layer, a cathode diffusion layer, a cathode channel, and a cathode bipolar plate. The computational domain includes: an anode bipolar plate, an anode channel, an anode diffusion layer, an anode catalyst layer, an electrolyte, a cathode catalyst layer, a cathode diffusion layer, a cathode channel, and a cathode bipolar plate.

3. The method for predicting the physical field response of SOEC under fluctuating renewable energy input as described in claim 1, characterized in that, The coupling of the gas multi-component mass fraction equation specifically includes: The following equation is used to express the gas multi-component mass fraction equation in single-channel co-electrolysis based on molecular free diffusion coupling, and the gas multi-component mass fraction equation in porous SOEC based on molecular free diffusion and Knudsen diffusion coupling co-electrolysis: ， ； in, For time, Porosity of porous media For the tortuosity of porous media, For apparent density, Components The mass fraction, For apparent speed, For Hamiltonian operators, For the effective diffusion coefficient, For the effective binary diffusion coefficient, The effective Knudsen diffusion coefficient.

4. The method for predicting the physical field response of SOEC under fluctuating renewable energy input as described in claim 2, characterized in that, The method of using fluctuating voltage as the operating voltage for numerical calculation of the SOEC transient model to construct the boundary conditions for numerical calculation specifically includes: The fluctuating voltage is used as the operating voltage for numerical calculations of the SOEC transient model. The boundary conditions for numerical calculation are formed by setting the outer boundary of the computational domain as an adiabatic boundary, setting the inlet of the anode and cathode channels as a mass flow inlet, setting the working pressure as a constant pressure, setting the boundary at the end face of the SOEC cell anode bipolar plate as the working voltage, and setting the boundary at the end face of the SOEC cell cathode bipolar plate as a reference potential of zero.

5. The method for predicting the physical field response of SOEC under fluctuating renewable energy input as described in claim 1, characterized in that, The method further includes: The single SOEC cell in the SOEC stack is symmetrically divided into two parts along the flow direction, and a symmetrical boundary is set at the division point. The part after division is then meshed. The numerical solution of the model equations to determine the dynamic responses of the SOEC component field and temperature field under fluctuating renewable energy input specifically includes: The model equations are numerically solved based on the meshed portion to obtain the dynamic response of the SOEC component field and temperature field under fluctuating renewable energy input in the meshed portion. Furthermore, the dynamic response of the SOEC component field and temperature field under fluctuating renewable energy input in the unmeshed portion is obtained through symmetry based on the symmetric boundary.

6. A device for predicting the physical field response of SOEC under fluctuating renewable energy input, characterized in that, The SOEC includes an SOEC stack, the SOEC stack includes multiple SOEC cells, and the device includes: The voltage determination module is used to determine the fluctuating voltage corresponding to the fluctuating renewable energy source based on the power-time fluctuation curve of the fluctuating renewable energy source. The modeling module is used to construct the boundary conditions for numerical calculation of SOEC transient model by taking the single channel of a single SOEC cell in SOEC stack as the computational domain and using the fluctuating voltage as the working voltage for numerical calculation of SOEC transient model. It couples multiple control equations in co-electrolysis to obtain the model equation set of SOEC transient model. The prediction module is used to determine the dynamic response of the SOEC component field and temperature field under fluctuating renewable energy input by numerically solving the model equations. Among them, various governing equations include the mass conservation equation, momentum conservation equation, energy conservation equation, gas multi-component mass fraction equation, electronic potential conservation equation, and ion potential conservation equation.

7. A computer-readable storage medium, characterized in that, The storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 5.

8. A computer device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the method as described in any one of claims 1 to 5.

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