Dynamic simulation method, device, equipment, medium and product of network-forming type fuel cell power generation system

Abstract

The invention discloses a dynamic simulation method, a dynamic simulation device, dynamic simulation equipment, a dynamic simulation medium and a dynamic simulation product for a network construction type fuel cell power generation system, and relates to the field of physical object simulation dynamic modeling and performance analysis. The method comprises the following steps: firstly, determining the overall structure and configuration of the network-constructing type fuel cell power generation system, carrying out comprehensive modeling by utilizing a thermoelectric comparison method on the basis, and establishing an overall standard circuit model of the network-constructing type fuel cell power generation system; constructing a dynamic simulation model of the network-constructing type fuel cell power generation system based on the overall standard circuit model; by adjusting input parameters of the dynamic simulation model, simulating dynamic responses of the network-forming type fuel cell power generation system in different distributed power generation scenes; and performing cluster analysis on simulation results under different distributed power generation scenes, and formulating an intelligent control strategy of the distributed power generation system. According to the method, the dynamic response of the network-forming type fuel cell power generation system to the power grid in the distributed power generation system can be accurately simulated under the condition that large-scale on-site deployment is not needed.

Description

Technical Field

[0001] The present application relates to the technical field of physical object simulation dynamic modeling and performance analysis, and particularly to a dynamic simulation method, device, equipment, medium and product for a networked fuel cell power generation system. Background Art

[0002] Driven by the current energy transformation and the goals of carbon peak and carbon neutrality, the integrated energy system, as an advanced energy supply system that is efficient, clean, economic, reliable and flexible, is a key research and development direction for promoting energy transformation and energy utilization technology innovation, and has received extensive attention and research. Fuel cells, with their high energy conversion efficiency and low emission characteristics, have shown great potential, especially in urban and industrial applications.

[0003] The networked fuel cell power generation system, as an efficient and low-carbon energy technology solution, is gradually becoming an important part of the energy structure optimization. The networked fuel cell power generation system can be directly connected to the existing power grid, effectively balance the power grid load, and improve the energy utilization efficiency. Its fuel cell system has tight coupling of heat, electricity and mass, with large differences in dynamic characteristics, and is complex in structure and expensive in cost. It is difficult to study its coupling dynamic characteristics through physical experiments. Summary of the Invention

[0004] The purpose of the present application is to provide a dynamic simulation method, device, equipment, medium and product for a networked fuel cell power generation system, which can accurately simulate the dynamic response of the networked fuel cell power generation system to the power grid in a distributed power generation system without large-scale field deployment.

[0005] To achieve the above purpose, the present application provides the following solutions.

[0006] In a first aspect, the present application provides a dynamic simulation method for a networked fuel cell power generation system, including: determining the overall structure and configuration of the networked fuel cell power generation system; based on the overall structure and configuration, using the thermoelectric analogy method for comprehensive modeling to establish an overall standard circuit model of the networked fuel cell power generation system; constructing a dynamic simulation model of the networked fuel cell power generation system based on the overall standard circuit model; by adjusting the input parameters of the dynamic simulation model, simulating the dynamic response of the networked fuel cell power generation system in different distributed power generation scenarios to obtain simulation results; and performing cluster analysis on the simulation results in different distributed power generation scenarios to formulate an intelligent control strategy for the distributed power generation system.

[0007] Optionally, the determining the overall structure and configuration of the networked fuel cell power generation system specifically includes:

[0008] The overall structure and configuration of the network-forming fuel cell power generation system include a solid oxide fuel cell system and power electronic devices; the solid oxide fuel cell system includes multiple parallel SOFC arrays; each SOFC array consists of multiple SOFC stack systems; each SOFC stack system includes a stack, a preheating subsystem, and an exhaust gas recovery subsystem; among them, the preheating subsystem consists of two preheaters; the exhaust gas recovery subsystem includes an exhaust gas burner, an exhaust gas pipeline, an air pipeline, and a fuel pipeline; the power electronic devices include a DC / DC converter and a DC / AC inverter for adjusting the output voltage of the fuel cell.

[0009] Optionally, based on the overall structure and configuration, the thermoelectric analogy method is used for comprehensive modeling to establish the overall standard circuit model of the network-forming fuel cell power generation system, specifically including: using the thermoelectric analogy method to model the thermal part of the SOFC stack system to establish a standard impedance model for the thermal part; using the thermoelectric analogy method to model the electrochemical part of the SOFC stack system to establish a standard impedance model for the electrochemical part; connecting the standard impedance model of the thermal part and the standard impedance model of the electrochemical part through the stack temperature T cell to couple and form the overall standard dynamic impedance model of the SOFC stack system; connecting the circuit model of the power electronic device and the overall standard dynamic impedance models of multiple SOFC stack systems through the SOFC array output voltage V out to establish the overall standard circuit model of the network-forming fuel cell power generation system.

[0010] Optionally, the use of the thermoelectric analogy method to model the thermal part of the SOFC stack system to establish a standard impedance model for the thermal part specifically includes: using the thermoelectric analogy method to convert the convective heat transfer process in the SOFC stack system into a resistance element, convert the heat capacity into a capacitance element, and equivalently convert the electrochemical heat generation process into a power source element; through the combination of resistance elements, capacitance elements, and power source elements, construct a standard impedance model for the thermal part to simulate the heat transfer path and temperature distribution in the SOFC stack system.

[0011] Optionally, the use of the thermoelectric analogy method to model the electrochemical part of the SOFC stack system to establish a standard impedance model for the electrochemical part specifically includes: according to the electrochemical reactions, polarization losses, ohmic losses, and concentration losses in the SOFC stack system, establish the mathematical equation of the stack output voltage V cell V cell = E cell - V act - V ohm - V conc ; where E cell is the corresponding Nernst potential; V act is the activation voltage drop; V ohm is the ohmic voltage drop; Vconc is the concentration voltage drop; according to the electrical output characteristics of the SOFC stack system, the mathematical equation of the output voltage V of the SOFC array is established out V out = N cell V cell ; where V cell is the output voltage of the stack; N cell is the number of plates.

[0012] Optionally, by adjusting the input parameters of the dynamic simulation model, the dynamic response of the grid-connected fuel cell power generation system under different distributed power generation scenarios is simulated to obtain the simulation results, specifically including: by changing the flow rates of fuel and air, the ambient temperature, and the load current at the beginning of the simulation, the dynamic response of the grid-connected fuel cell power generation system under different distributed power generation scenarios is simulated to obtain the thermoelectric output parameters V out and T cell as well as the dynamic variation law of the power P out output from the SOFC array to the power grid over time as the simulation result; where P out = V out ·I; I is the load current.

[0013] Optionally, the simulation results under different distributed power generation scenarios are cluster-analyzed to formulate an intelligent control strategy for the distributed power generation system, specifically including:

[0014] Based on the control strategy P out (t) = P ref + K p ·(P set (t) - P out (t)), the output power P out (t) at time t is dynamically adjusted according to the grid demand; where P ref is the reference power; K p is the proportional gain; P set (t) is the set power at time t;

[0015] When an abnormality occurs in the system, based on the control strategy under abnormal conditions the power adjustment amount ΔP out (t) at time t is determined; where K d is the derivative gain; E errot (t) is the energy error at time t; based on the power adjustment amount ΔP out (t) at time t, the output power P out (t) at time t is adjusted.

[0016] In a second aspect, the present application provides a dynamic simulation device for a network-forming fuel cell power generation system, including: a system overall structure and configuration determination module for determining the overall structure and configuration of the network-forming fuel cell power generation system; an integrated modeling module for performing integrated modeling based on the overall structure and configuration using the thermoelectric analogy method to establish an overall standard circuit model of the network-forming fuel cell power generation system; a dynamic simulation model construction module for constructing a dynamic simulation model of the network-forming fuel cell power generation system based on the overall standard circuit model; a dynamic response simulation module for simulating the dynamic response of the network-forming fuel cell power generation system under different distributed power generation scenarios by adjusting the input parameters of the dynamic simulation model to obtain simulation results; and a cluster analysis module for performing cluster analysis on the simulation results under different distributed power generation scenarios to formulate an intelligent control strategy for the distributed power generation system.

[0017] In a third aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the dynamic simulation method for the network-forming fuel cell power generation system.

[0018] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, it implements the dynamic simulation method for the network-forming fuel cell power generation system.

[0019] In a fifth aspect, the present application provides a computer program product including a computer program, and when the computer program is executed by a processor, it implements the dynamic simulation method for the network-forming fuel cell power generation system.

[0020] According to the specific embodiments provided by the present application, the following technical effects are disclosed by the present application:

[0021] The present application provides a dynamic simulation method, device, equipment, medium, and product for a network-forming fuel cell power generation system, which can not only evaluate the performance of the network-forming fuel cell power generation system without large-scale on-site deployment, but also predict the dynamic response of the network-forming fuel cell power generation system to the power grid in a distributed power generation system, greatly improving the efficiency and accuracy of simulation. The established overall standard circuit model of the network-forming fuel cell power generation system provides a scientific basis for the design and operation optimization of the distributed power generation system, can support the collaborative optimization and intelligent control of multi-energy systems, and further promotes the development and application of the integrated energy system. Description of the Drawings

[0022] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required for the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on these drawings.

[0023] Figure 1 It is a schematic flow diagram of a dynamic simulation method for a network-forming fuel cell power generation system of the present application;

[0024] Figure 2 It is a structural diagram of a network-forming fuel cell power generation system;

[0025] Figure 3 It is a structural diagram of an SOFC stack system;

[0026] Figure 4 It is a schematic diagram of the structure and heat transfer process inside a fuel cell;

[0027] Figure 5 It is a structural diagram of a single-layer casing heat exchanger;

[0028] Figure 6 It is a schematic diagram of the thermal part standard impedance model of an SOFC stack system;

[0029] Figure 7 It is a schematic diagram of the electrochemical part standard impedance model of an SOFC stack system;

[0030] Figure 8 It is a schematic diagram of the overall standard dynamic impedance model of an SOFC stack system;

[0031] Figure 9 It is a schematic diagram of the overall standard circuit model of a network-forming fuel cell power generation system;

[0032] Figure 10 It is a schematic diagram of the thermoelectric dynamic response characteristics of a fuel cell under load mutation;

[0033] Figure 11 It is a schematic diagram of the temperature dynamic response characteristics of the preheating subsystem of a fuel cell under load mutation;

[0034] Figure 12 It is a schematic diagram of the thermoelectric dynamic response characteristics of a fuel cell under fuel flow mutation;

[0035] Figure 13 It is a schematic diagram of the temperature dynamic response characteristics of the preheating subsystem of a fuel cell under fuel flow mutation. Specific embodiments

[0036] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying 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. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the protection scope of the present application.

[0037] In view of the defects existing in the prior art, the present application proposes a dynamic simulation method, device, equipment, medium and product for a network-forming fuel cell power generation system, which can accurately simulate the dynamic response of the network-forming fuel cell power generation system to the power grid in a distributed power generation system without large-scale on-site deployment.

[0038] To make the above objects, features and advantages of the present application more obvious and understandable, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0039] In an exemplary embodiment, as Figure 1 shown, a dynamic simulation method for a network-forming fuel cell power generation system is provided, including the following steps 1 to 5.

[0040] Step 1: Determine the overall structure and configuration of the network-forming fuel cell power generation system.

[0041] First, determine the modeling object of the present application as the network-forming fuel cell power generation system. Then select the configuration and structure of the network-forming fuel cell power generation system and conduct architecture design. The network-forming fuel cell power generation system described in the present application mainly consists of a solid oxide fuel cell (SOFC) system and power electronic devices. As Figure 2 shown, the solid oxide fuel cell system includes a plurality (N) of parallel SOFC arrays, and each SOFC array consists of a plurality of SOFC stack systems. As Figure 3 described, each SOFC stack system includes an electric stack (abbreviation for fuel cell stack), a preheating subsystem, and an exhaust gas recovery subsystem. Among them, the preheating subsystem consists of two preheaters, namely the first preheater and the second preheater. The exhaust gas recovery subsystem includes an exhaust gas burner, an exhaust gas pipeline, an air pipeline, and a fuel pipeline.

[0042] As Figure 2As shown in the figure, the power electronic device includes a DC / DC (direct current / direct current) converter and a DC / AC (direct current / alternating current) inverter for regulating the output voltage of the fuel cell. Through these devices, the active and reactive power flowing from the fuel cell distributed power source to the power grid can be controlled. During the design process, special consideration is given to its integration and compatibility in the distributed generation system to ensure that the system can operate in coordination with other distributed energy systems. The goal of this step is to determine the overall structure and configuration of the system, providing a basis for subsequent modeling and simulation.

[0043] Based on the structure of the modeling object, clarify its operation process and mechanism, and obtain characteristic parameters representing heat and mass transfer, electrochemical reactions, and power electronic characteristics within the system, mainly including: the flow rates of air and fuel participating in the reaction, the inlet temperatures of air and fuel flowing into the system, the pressure inside the stack, the volume of the gas channels and the cell length within the fuel cell, the length, diameter, and density of the gas transmission pipelines, the heat transfer coefficients of each heat transfer unit, as well as the heat capacities of air, fuel, cells, and pipelines. The acquisition of these parameters is for accurate mathematical modeling and simulation analysis in subsequent steps. By obtaining these parameters, ensure that the model can accurately reflect the dynamic behavior of the system under different operating conditions.

[0044] Step 2: Based on the overall structure and configuration, use the thermoelectric analogy method for comprehensive modeling to establish an overall standard circuit model of the grid-connected fuel cell power generation system.

[0045] Taking the characteristic parameters and system operation mechanism obtained in Step 1 as a reference, clarify the mathematical formulas and models describing the dynamic change laws of each component and process. For convenience in modeling, divide the mathematical formulas and models into a thermal part, an electrochemical part, and a power electronic part according to the parameter types they describe, and at the same time consider how these models interact with other distributed energy devices in the distributed generation system to achieve the overall optimization of the system.

[0046] The specific content of Step 2 includes:

[0047] Step 2.1: Use the thermoelectric analogy method to model the thermal part of the SOFC stack system and establish a standard impedance model for the thermal part.

[0048] To accurately describe the thermoelectric coupling characteristics of the SOFC stack system, first, it is necessary to perform circuit equivalent processing on each thermal process in the system, mainly including the internal heat transfer process of the fuel cell (abbreviation of SOFC, further abbreviated as cell), the preheating process of the preheater, and the heat generation process of the tail gas combustion.

[0049] First, clarify the internal heat transfer process of a single fuel cell. The SOFC adopted in this application is a tubular one, and its internal structure and heat transfer process schematic diagram are as Figure 4As shown. Air is supplied through the air supply duct (AST) and forced to return inside the cell. Fuel flows outside the cell and is parallel to the air. The internal heat transfer of the fuel cell mainly occurs in the forms of radiation, convection, and mass flow.

[0050] The SOFC is an exothermic reaction, and the heat release Q of its internal chemical reaction gen can be expressed as

[0051] Q gen = Q chem - Q elec (1)

[0052] Q chem is the available work released by the chemical reaction, also known as electrochemical heat generation, and its calculation formula is

[0053] Q chem = nΔH(2)

[0054] where ΔH is the change in enthalpy of the internal chemical reaction of the SOFC. n represents the molar flow rate of hydrogen consumption, with the unit mol / s.

[0055] Q elec is the output electric power of the fuel cell, defined as

[0056] Q elec = I·V cell (3)

[0057] where I is the load current (A) of the SOFC, and V cell is the output voltage (V) of a single fuel cell.

[0058] Q rad is the radiative heat transfer amount between the cell and the air supply duct, expressed as

[0059]

[0060] where σ is the surface emissivity, and ε AST is the Stefan - Boltzmann constant. T cell and T AST are the temperatures of the stack and the air supply duct respectively. A AST,outer represents the outer area of the air supply duct, with the unit m2.

[0061] In the stack, air is sent into the interior of the cell (cathode surface) through the air supply pipe and then returns to the open end, while the fuel (a mixture of hydrogen and water vapor) flows parallel to the air over the exterior of the cell (anode surface). The temperature difference between adjacent fuel cells can be neglected, and heat transfer within each fuel cell mainly occurs through radiation, convection, and heat conduction. In the fuel cell, the temperature of the air when it enters the AST is T air,o , and the temperature of the air when it exits the AST is T air,i . Subsequently, it undergoes convective heat transfer with the AST at temperature T AST , and the heat transfer quantity is represented by Q conv,AST,inner . Inside the cell, the air undergoes convective heat transfer with the AST and the cell respectively, and the heat transfer quantities are represented by Q conv,AST,outer and Q conv,air respectively. Meanwhile, the temperature of the fuel when it enters the cell is T fuel,o , and the temperature of the fuel when it exits the cell is T fuel,i . Then it undergoes convective heat transfer with the cell, and the heat transfer quantity is represented by Q conv,fuel . In addition, there is radiative heat transfer between the cell and the AST, which is represented by Q rad . Q flow,air,cell , Q flow,air,AST , and Q flow,fuel are defined as the inlet and outlet heat exchanges in the AST and the cell due to different mass flows.

[0062] Q flow,air,cell = G air (T aircell,i - T aircell,o ) (5)

[0063] Q flow,air,AST = G air (T air,i - T air,o ) (6)

[0064] Q flow,fuel = G fuel (T fuel,i - T fuel,o ) (7)

[0065] Among them, Q flow,air,cell represents the heat carried away by the air flowing in the cell; Q flow,air,AST represents the heat carried away by the air flowing in the air supply pipe; Q flow,fuel represents the heat carried away by the fuel flowing in the cell; T aircell,i represents the dynamic temperature of the air in the cell; T aircell,o represents the temperature of the air at the inlet of the cell.

[0066] As mentioned above, the convective heat transfer in the four spaces inside the fuel cell is Q conv,AST,inner , Qconv,AST,outer , Q conv,air and Q conv,fuel , which can be regarded as being driven by four linear temperature differences (T AST -T air,i , T aircell,o -T AST , T cell -T aircell,i and T cell -T fuel,i ) and flowing through four thermal resistances R conv,AST,inner , R conv,AST,outer , R conv,air and R conv,fuel to generate the heat transfer amount. Among them, the temperature differences of the fluids before and after entering the battery, such as T fuel,i –T fuel,o , T aircell,i –T aircell,o and T air,i –T air,o can be analogized to additional thermomotive forces (such as electromotive forces). The increased thermomotive force is the ratio of the heat transfer amount Q to the heat capacity flow G of the corresponding fluid. The above four thermal resistances are defined as

[0067]

[0068]

[0069]

[0070]

[0071] Among them, G air and G aircell are defined as the heat capacity flows of AST and the air in the battery; G fuel is the heat capacity flow of the fuel; they are respectively the products of the mass flow rate of the fluid and the heat capacity. a air1 =(K1A1) / G air , a air2 =(K2A2) / G aircell , a air3 =(K3A3) / G aircell , a fuel =(K4A4) / G fuel . K1, K2, K3, and K4 are the thermal conductivities of the four heat exchange processes respectively, and A1, A2, A3, and A4 are the heat transfer areas of the four heat exchange processes respectively.

[0072] The function of the preheater is to preheat the air and hydrogen entering the stack using the high-temperature flue gas generated by the burner. The preheater of this application adopts a single-layer sleeve structure, such as Figure 5As shown, an air duct and a fuel duct are provided in the flue gas duct. The cold fluids (air and fuel hydrogen) in the air duct and the fuel duct flow countercurrently to the hot fluid (high-temperature flue gas) in the flue gas duct, and the cold and hot fluids exchange heat through the metal duct wall: the flue gas at temperature T bur flows into the flue gas duct and exchanges convective heat with the air duct at temperature T air,tube and the fuel duct at temperature T fuel,tube , and the heat exchange amounts are Q conv,bur,airtube and Q conv,bur,fueltube respectively. At the same time, the air at temperature T air and the hydrogen at temperature T fuel enter the ducts and exchange convective heat with the air duct and the fuel duct respectively, and the heat exchange amounts are Q conv,air,tube and Q conv,fuel,tube respectively.

[0073] In the preheater, it is mainly convective heat exchange. R conv,bur,airtube , R conv,bur,fueltube , R conv,air,tube and R conv,fuel,tube are the four thermal resistances of the heat exchange process, and Q conv,bur,airtube , Q conv,bur,fueltube , Q conv,air,tube and Q conv,fuel,tube are the heat exchange amounts of the four thermal resistances, which are driven by four linear temperature differences, namely T bur -T air,tube , T bur -T fuel,tube , T air,tube -T air and T fuel,tube -T fuel . The thermal resistances and heat exchange amounts of the above four heat exchange processes are defined as follows respectively.

[0074] Heat exchange process between the tail gas and the air duct:

[0075]

[0076]

[0077] Heat exchange process between the tail gas and the fuel duct:

[0078]

[0079]

[0080] Heat exchange process between the air duct and the air:

[0081]

[0082]

[0083] Heat exchange process between the fuel pipeline and the fuel:

[0084]

[0085]

[0086] Wherein, G bur 、G air and G fuel are defined as the heat capacity flows of the tail gas, air, and fuel, respectively, which are the products of the mass flow rate of the fluid and the heat capacity. a bur,air =(KA) bur,air / G bur ; a bur,fuel =(KA) bur,fuel / G bur ,a air =(KA) air / G air ,a fuel =(KA) fuel / G fuel . Among them, (KA) bur,air 、(KA) bur,fuel 、(KA) air 、(KA) fuel are the products of the heat transfer coefficient K and the heat transfer area A of the four heat exchange processes respectively.

[0087] Based on the above analysis of the heat exchange process of the SOFC stack system, the overall energy balance equations (20)-(25) can be listed:

[0088]

[0089]

[0090]

[0091]

[0092]

[0093]

[0094] Wherein, C AST 、C cell 、 and They are the heat capacities of the air supply duct, the battery, the air duct 1(2), and the fuel duct 1(2), respectively. Since the preheating subsystem has two consecutive identical preheaters, namely the first preheater and the second preheater, with the same structure and heat transfer process formulas, the relevant parameters of the two preheaters in this application are distinguished by subscripts 1 and 2, where subscript 1 corresponds to the relevant parameters of the first preheater and subscript 2 corresponds to the relevant parameters of the second preheater. Specifically, T air,cell is the temperature of air in the battery; T fuel,cell is the temperature of fuel in the battery; G air,cell is the heat capacity flow of air in the battery, which is the product of the mass flow rate of air and the heat capacity; a air =(KA) aircell / G air,cell ; (KA) aircell is the product of the heat transfer coefficient K and the heat transfer area A. T air,AST is the temperature of air in the air supply duct; T air,cell is the temperature of air in the battery. T airtube1 and T airtube2 are the temperatures of the air ducts in the first preheater and the second preheater, respectively. T bur1 and T bur2 are the temperatures of the high-temperature exhaust gas in the first preheater and the second preheater, respectively. T air,in is the initial temperature of air entering the system. T air1 and T air2 are the temperatures of air in the first preheater and the second preheater, respectively. T fuel,in is the initial temperature of fuel entering the system. R conv,bur,airtube1 and R conv,bur,airtube2 are the heat transfer resistances between the high-temperature exhaust gas and the air duct in the first preheater and the second preheater, respectively; R conv,air,tube1 and R conv,air,tube2 are the heat transfer resistances between the air duct and the air in the first preheater and the second preheater, respectively. R conv,bur,fueltube1 and R conv,bur,fueltube2 are the heat transfer resistances between the high-temperature exhaust gas and the fuel duct in the first preheater and the second preheater, respectively; R conv,fuel,tube1 and R conv,fuel,tube2 are the heat transfer resistances between the fuel duct and the fuel in the first preheater and the second preheater, respectively; T fuel1 、T fuel2 are the temperatures of fuel in the first and second preheaters, respectively; T air,in and T fuel,in are the inlet temperatures of the air and fuel entering the preheater, which are the initial temperature of the whole system or the ambient temperature.

[0095] The preheater and the heat exchange process of the stack in the SOFC stack system are identified and analyzed through the above steps. Using the thermoelectric analogy method, these heat processes are converted into corresponding circuit elements. The convective heat transfer process is converted into a resistance element, the heat capacity is converted into a capacitance element, and the electrochemical heat generation process is equivalent to a power source element. Through the combination of these circuit elements, a standard thermal impedance model characterized by dynamic resistance and capacitance is constructed, called the standard impedance model of the thermal part, as Figure 6 shown, which is used to simulate the heat transfer path and temperature distribution in the system. From Figure 6 the standard impedance model of the thermal part in, it can be seen that the energy balance equations of the 6 grounded temperature nodes in formulas (20) to (25) have covered all temperature nodes by analogy with Kirchhoff's node voltage equation.

[0096] The research highlight of this application is to redefine the dynamic thermal resistance of the heat exchange process. For the thermal / electrical / mass coupled transport process, the coupled process parameters (driving potential, dissipation, flow) are redefined. Based on the heat exchange components, an integrated model that separates the expression of process parameters from the definition formula of component characteristics is proposed based on energy dissipation and the thermal Ohm's law. The thermal resistance of various types of heat exchangers is standardized, and the standard thermal resistance of heat exchangers that reflects the characteristics of energy conservation and delay characteristics is proposed to achieve the integrated analysis of multi-process coupling. This standard impedance model of the thermal part provides a necessary basis for the integration of the electrochemical and power electronics part models in the subsequent steps, ensuring that the thermoelectric coupling relationship in the overall model of the system can be accurately simulated and optimized.

[0097] Step 2.2: Use the thermoelectric analogy method to model the electrochemical part of the SOFC stack system and establish the standard impedance model of the electrochemical part.

[0098] After completing the equivalent modeling of the heat exchange process, in order to ensure the unity of the model, it is necessary to establish an electrochemical equivalent circuit model of the fuel cell system as the standard impedance model of the electrochemical part to accurately describe the electrochemical reaction process in the fuel cell.

[0099] In this step 2.2, key electrochemical processes such as electrode reactions, electron conduction, and ion conduction are identified and converted into circuit elements. The open-circuit voltage source represents the initial voltage of the fuel cell, the polarization resistance and the concentration difference resistance respectively describe the voltage losses caused by electrode reactions and concentration gradients, and the double-layer capacitance simulates the capacitance characteristics of the electrode surface. Through the combination of these elements, a complete standard impedance model of the electrochemical part is constructed. The construction of this standard impedance model of the electrochemical part is not only closely related to the thermoelectric model, but also provides a solid foundation for the subsequent model integration. By integrating the standard impedance models of the thermal part and the electrochemical part, a unified system dynamic model can be formed to comprehensively simulate the performance of the SOFC stack system under different distributed power generation scenarios, thereby providing a scientific basis for system optimization and the formulation of control strategies.

[0100] First, according to the electrical output characteristics of the SOFC stack system, establish the mathematical equation for the output voltage V of the SOFC array, as shown in Equation (26). out as follows.

[0101] V out = N cell V cell (26)

[0102] where V cell is the output voltage of the stack; N cell is the number of plates.

[0103] Based on the electrochemical reaction, polarization loss, ohmic loss, and concentration loss within the SOFC stack system, establish the mathematical equation for the output voltage V of the stack: cell as follows:

[0104] V cell = E cell - V act - V ohm - V conc (27)

[0105] where E cell is the corresponding Nernst potential; V act is the activation voltage drop; V ohm is the ohmic voltage drop; V conc is the concentration voltage drop.

[0106] Among them, the concentration voltage drop

[0107]

[0108] R is the gas constant, with a value of 8.3143 J / (mol K). F is the Faraday constant, with a value of 96487 C / mol). are the effective partial pressures of hydrogen, oxygen, and water vapor respectively; are the actual partial pressures of hydrogen, oxygen, and water vapor at the cathode and anode respectively.

[0109] The ohmic voltage drop

[0110] V ohm = V electrodes + V ohm,elecyt + V ohm,interc = IR ohm (29)

[0111] V electrodes is the electrode resistance; V ohm,elecyt is the electrolyte resistance; V ohm,interc is the interconnection resistance between cells; R ohmis an ohmic resistance.

[0112]

[0113] a (Ωm) and b (K) are constants; A cell is the effective area of the battery (m2); δ elecyt is the thickness of the electrolyte; δ interc is the thickness of the battery interconnection line; A interc is the area of the battery interconnection line.

[0114] Activation voltage drop

[0115]

[0116] z is the number of electrons involved; I0 is the exchange current (A), and I is the actual load current.

[0117] Nernst potential

[0118]

[0119] E0 is the standard reference potential at 298K and 1atm pressure under standard conditions.

[0120]

[0121] k E is a constant term. represents the standard reference potential under standard conditions (298K and 1 atm).

[0122] In addition, the following equation holds

[0123]

[0124] C dl represents the equivalent capacitance; V C and I C are the voltage and current of the equivalent capacitance C respectively; V dl = V C ; R act is the activation resistance; R conc is the concentration resistance; t is the reaction time.

[0125] In a SOFC array composed of multiple SOFC stack systems, the output voltage V out is jointly determined by multiple factors, including electrochemical reactions, ohmic losses, concentration losses, and polarization losses, etc. Specifically, Equation (28) represents the concentration voltage drop V conc , which depends on the partial pressures of gases (such as the partial pressures of hydrogen and oxygen). The temperature T affects the diffusion coefficient and solubility of gases, thereby changing the gas partial pressures, and further affecting the concentration voltage drop V concIn addition, Equations (29) and (30) calculate the ohmic voltage drop V ohm , which is closely related to the resistivity of the material, and the resistivity itself is a function of temperature. By means of the thermoelectric coupling model, the change of temperature T can be simulated, and the direct influence of the change of material resistivity on the ohmic voltage drop V ohm can be accurately reflected. Equation (31) calculates the activation voltage drop V act , which depends on the activation energy of the electrode reaction, and the activation energy is significantly affected by temperature. Generally, an increase in temperature will reduce the activation energy, thereby reducing the activation voltage drop V act . It can be seen from the above equations that the calculation process of each voltage is not only related to the electrochemical characteristics of the battery, but also affected by a key factor, that is, the battery temperature T, and the battery temperature T is inseparable from the heat transfer process in the SOFC stack system described above. Accordingly, a close thermoelectric coupling relationship is formed in the SOFC stack system. According to the above electrochemical reaction process, an equivalent standard impedance model of its electrochemical part is constructed as Figure 7 shown. Figure 7 The voltage drops corresponding to E cell , R act , R conc , R ohm and C dl in Figure 7 cell have been given by Equations (30) to (34). The node V

[0126] is the output voltage of a single fuel cell.

[0126] Step 2.3: Connect the thermal part standard impedance model and the electrochemical part standard impedance model through the stack temperature T cell to couple them into the overall standard dynamic impedance model of the SOFC stack system.

[0127] Equivalent the thermal part standard impedance model and the electrochemical part standard impedance model into a unified circuit model is of great significance. The purpose of this equivalent modeling is to simplify the complex physical process of the SOFC stack system into a circuit model that can be calculated and analyzed, so as to better understand and predict the overall behavior of the system. The influence of temperature changes caused by thermoelectric coupling on electrochemical reactions is directly reflected in components such as resistors and capacitors in the circuit. By coupling the thermal process and the electrochemical process in the circuit model, a complete system model with accurate dynamic response can be created, which is crucial for the design and optimization of the system.

[0128] Based on the idea of thermoelectric analogy, this application takes the RC circuit in the circuit as a reference, analogizes the dynamic change law of thermal energy with electrical energy, and then passes the thermal part standard impedance model composed of thermal resistance (Equations (8) to (19)) and thermal capacitance (Equations (20) to (25)) and the standard impedance model of the electrochemical part (Equations (26) to (34)) through the key node parameter Tcell Make connections and couple them into a unified power flow model, that is, couple them into the overall standard dynamic impedance model of the SOFC stack system, as Figure 8 shown. In this coupled model, the energy transfer and conversion processes between the thermal part and the electrochemical part are integrated into an overall standard dynamic impedance model. Specifically, the electrochemically generated heat Q chem After being adjusted by the thermal resistance and thermal capacitance within the SOFC stack system, it ultimately leads to a temperature change at node T cell . This temperature change directly affects the rate of the electrochemical reaction and the generation of the electromotive force. Finally, this thermal energy is converted into electrical energy, driving the electromotive force in the electrochemical part to generate current and enter the power system. In this way, the coupled overall standard dynamic impedance model not only accurately describes the energy transfer path within the SOFC stack system, but also ensures that the overall model can truly simulate the thermoelectric coupling effect of the system, thereby providing an accurate simulation platform for subsequent system optimization and the control of power electronic devices. The overall standard dynamic impedance model of the energy transmission of the integrated SOFC stack system is as Figure 8 shown. This model can simultaneously simulate the thermoelectric response of the fuel cell and optimize the output voltage performance of the fuel cell. Through this integration, the simulation system can provide more accurate output voltage predictions and can be used to analyze the performance of the system under different operating conditions, such as the response under different loads and temperature conditions, thereby providing a reference for the optimal design of the system.

[0129] Step 2.4: Connect the circuit model of the power electronic device with the overall standard dynamic impedance model of multiple SOFC stack systems through the output voltage V of the SOFC array out to establish the overall standard circuit model of the grid-forming fuel cell power generation system.

[0130] The above process constructs the overall standard dynamic impedance model of the SOFC stack system. As can be seen from Figure 8 , the output of this model is node V cell . This lays the foundation for the combination with subsequent power electronic devices. The power electronic devices of this application mainly include DC / DC and DC / AC converters. DC / DC conversion can boost the output voltage of the fuel cell to a DC voltage, and DC / AC can convert DC power into an AC voltage synchronized with the grid with a specific amplitude and frequency. As Figure 9 shown, connect the power electronic device to the output node V of the SOFC stack system cell (that is, connect to the output voltage V of the SOFC array outFor connection), it can handle the voltage and current output of the fuel cell, ensure its stable grid connection operation, and perform real-time regulation. The integration of this model enables the overall optimization of the system, ensuring that the fuel cell system can operate efficiently and safely under different working conditions and be coordinated with the grid requirements.

[0131] In the embodiment of this application, an SOFC array is composed of 8 SOFC stacks of 5 kW in 4 series and 2 parallel, and 12 SOFC arrays of 40 kW are connected in parallel to form an SOFC power plant. The DC / DC boost converter adopts 200V / 480V, 500 kW for each. The three-phase DC / AC inverter adopts DC480V / AC208V, 500 kW. The overall standard circuit model of the grid-connected fuel cell power generation system after combining the fuel cell power plant containing multiple SOFC arrays with power electronic devices is as Figure 9 shown. This overall standard circuit model not only simulates the heat transfer path and temperature distribution inside the fuel cell system, but also describes the electrochemical characteristics in the fuel cell system. Through the interaction and integration with the power system, it can simulate the thermoelectric dynamic response of the fuel cell system during grid-connected operation and the impact of these changes on the output voltage and system stability, enabling the fuel cell system to achieve efficient and safe grid-connected operation in practical applications.

[0132] Step 3: Based on the overall standard circuit model, construct a dynamic simulation model of the grid-connected fuel cell power generation system.

[0133] Based on the overall standard circuit model of the grid-connected (grid-forming) fuel cell power generation system constructed in the above Step 2, using the Matlab programming language, equations are input to establish the dynamic simulation model of this overall standard circuit model. Among them, formulas (1) to (25) are used to describe the heat transfer and dynamic changes of temperature in the system, including the calculation of thermal resistance and thermal capacitance. These equations help simulate the temperature distribution and heat flow inside the fuel cell system, especially the temperature changes at the key node T cell . Formulas (26) to (34) are used to describe the voltage drops in the electrochemical process (such as ohmic voltage drop, concentration voltage drop, and activation voltage drop). These equations, combined with the thermoelectric coupling equations (1) to (25), simulate the impact of temperature changes on the electrochemical reaction rate and electromotive force generation. Based on the integration of the thermal part standard impedance model (20) to (25) and the electrochemical part standard impedance model (26) to (34), an overall standard dynamic impedance model is established. This model realizes the conversion of thermal energy into electrical energy through the coupling node T cell and describes the energy transfer process between the thermal part and the electrochemical part through equations.

[0134] Input the necessary characteristic parameters (such as the stack air pressure, ambient temperature, mass flow rates of fuel and air, load current, volumes and masses of the anode and cathode of the stack, radius, length, and density of the preheater pipeline) into the dynamic simulation model. After setting the initial operating conditions (initial values of the dynamic changes of each temperature node, defaulting to the ambient temperature), perform the simulation. Compare the simulation results with the actual operating data of the object. When the average error between the simulation results (stack output voltage V cell and temperature T cell ) and the reference model (results of the reference literature) is less than 5%, it indicates that the requirements are met, and save the temperatures of each node and the stack voltage output by the model.

[0135] Step 4: By adjusting the input parameters of the dynamic simulation model, simulate the dynamic response of the grid-connected fuel cell power generation system under different distributed generation scenarios to obtain the simulation results.

[0136] Using the established dynamic simulation model, by changing the flow rates of fuel and air G fuel and G air , ambient temperature (initial condition), load current I, etc. at the beginning of the simulation to simulate the situation of the fuel cell system under actual working scenarios. Then, the thermoelectric output parameters V out and T cell of the SOFC stack system and the power P out output from the SOFC array to the power grid (the product of the output voltage V out and the load current I) can be obtained as the dynamic change law over time as the simulation results.

[0137] By dynamically monitoring the thermoelectric output parameters of the fuel cell (stack temperature T cell and output voltage V out ), the performance of the system under different loads and operating conditions can be understood. Mastering these change laws helps to optimize the operating parameters of the fuel cell (such as fuel flow rate, temperature control, load current, etc.), thereby improving the overall efficiency and stability of the system.

[0138] Step 5: Perform cluster analysis on the simulation results under different distributed generation scenarios to formulate an intelligent control strategy for the distributed generation system.

[0139] Under different distributed generation scenarios, by adjusting the input parameters in the dynamic simulation model (such as ambient temperature, load fluctuations, fuel supply conditions, etc.), various external environments and operating conditions can be simulated. Performing cluster analysis on the simulation results can evaluate the operating performance of the grid-connected fuel cell power generation system under different operating conditions and optimize the operating strategy. For example, by adjusting the key parameters of the system, such as G air and G fuel in formulas (16) and (18)And parameters such as the load current I in formulas (29) to (34) can optimize the overall performance of the system, enabling it to operate efficiently under different conditions. For example, increasing the fuel flow rate G fuel can accelerate the rate of the electrochemical reaction and improve the output power of the fuel cell stack. The fuel flow rate G fuel is closely related to the fuel cell temperature T cell and the output voltage V out Flow regulation can optimize the efficiency of the electrochemical reaction, thereby stabilizing and increasing the voltage output of the battery. Increasing the load current I density will exacerbate the ohmic voltage drop V ohm , which is mainly reflected in the increased resistance loss inside the battery. As the current density increases, the output voltage will decrease, resulting in reduced efficiency. Therefore, the current density must be within an appropriate range to balance power output and losses.

[0140] Furthermore, an intelligent control strategy is formulated based on the simulation analysis results to ensure the safe, stable, and efficient operation of the distributed generation system. The intelligent control system needs to monitor the output power of the fuel cell in real time and dynamically adjust the output power P out (t). The control strategy can be implemented through the following power control formula:

[0141] P out (t) = P ref + K p ·(P set (t) - P out (t)) (35)

[0142] where P out (t) is the output power at time t; P ref is the reference power; K p is the proportional gain; P set (t) is the set power at time t.

[0143] When an abnormality occurs in the system, the intelligent control system needs to respond quickly and adjust the operating parameters. The control strategy in abnormal situations can be based on the following formula:

[0144]

[0145] where ΔP out (t) is the power adjustment amount at time t. K d is the derivative gain, which is a gain parameter used in the intelligent control system to respond to the rate of change of the error. It determines the sensitivity of the control system to the change of the error. When an abnormality occurs in the system, increasing K d will accelerate the response speed of the system to the change of the error and reduce the power fluctuation caused by the sudden change of the error. On the contrary, decreasing K dIt will make the system response smoother, but may have a slower response. E errot E(t) is the energy error at time t. This term represents the rate of change of the error between the current state of the system and the set target over time. In abnormal situations, the rate of change of the error may increase, indicating that the system state rapidly deviates from the set target. By adjusting E errot (t), the response speed of the system to this change in error can be controlled to avoid excessive fluctuations.

[0146] Suppose a grid-forming fuel cell power generation system is operating in the power grid and there is a sudden increase in load, resulting in an imbalance in the system output power. In this case, the system can increase K d to quickly respond to this sudden change in error, and based on the power adjustment amount ΔP out (t) at time t, adjust the output power ΔP out (t) in a timely manner to ensure the stability of the power grid.

[0147] This application first combines the standard impedance model of the fuel cell and the circuit diagram of the power electronic device to realize the grid-forming integrated modeling of the fuel cell system and the power electronic device, and establishes the overall standard circuit model of the grid-forming fuel cell power generation system; on this basis, a high-precision dynamic simulation model is constructed to realize the high-precision simulation of the variation laws of various types of parameters and the load response characteristics under multiple scenarios. By simulating different operating environments, the cluster analysis and management optimization of operating parameters such as voltage, current, flow rate, and temperature in the microgrid can be realized, effectively improving the reliability and flexibility of the distributed power generation system, and providing strong technical support for the development of the smart grid.

[0148] In the following embodiments, for a 480kW SOFC power plant formed by paralleling 12 40kW SOFC arrays, the necessary relevant physical property parameters for its operation are collected, and its dynamic response characteristics are modeled and analyzed using the method of this application, as follows. Taking the change of fuel flow rate, inlet temperature, and load as examples, the model and analysis method of this application are applied, so that when analyzing the dynamic response, the responses of each component of the fuel cell are combined, and the comprehensive response characteristics after their mutual coupling are considered for collaborative analysis.

[0149] Set the load current to 100A and suddenly change to 50A at 7000s, the air and fuel inlet temperatures are 275K, and the fuel flow rate is 1×10 -3 mol / s (90% hydrogen + 10% water). Figure 10 and Figure 11 show the temperature and voltage changes of each module of the SOFC stack system. The abscissa is time (Time), and the ordinate is the temperature T or voltage. Before the mutation occurs, the air and fuel outlet temperatures T air1 and Tfuel1 are 578K and 651K respectively, and the combustion gas outlet temperature T bur1 is 1050K; after passing through the first preheater, the air and fuel outlet temperatures T air2 and T fuel2 are 843K and 945K respectively, where the combustion gas outlet temperature T bur2 is 1240K. Finally, the outlet temperature T cell of the fuel cell stack is 1002K, and the output voltage V out is 38V.

[0150] Figure 10 shows that due to the decrease in the load current, the temperature of the fuel cell stack gradually decreases to 922K, the voltage reaches an instantaneous peak of 69V, and then gradually decreases to 58V. Although the heat generation rate Q gen drops immediately, due to the thermal inertia of the stack materials, the temperature does not drop immediately, but delays and gradually decreases. In addition, as the current decreases, the overpotentials (activation overpotential and ohmic overpotential) also decrease. The activation overpotential decreases with the reduction of the current demand. The ohmic losses caused by the internal resistance of the cell components also decrease, resulting in an overall increase in the output voltage. After the initial increase, the voltage gradually decreases because as the fuel cell stack adapts to the new thermal equilibrium, the reaction needs to re-establish the equilibrium conditions, including ion migration and distribution, overpotential adjustment, and electrode reactions, etc.

[0151] Figure 11 shows the temperature changes of each gas in the first preheater and the second preheater. When a mutation occurs, the dynamics of the entire system change. As the current decreases, the intensity of the electrochemical reaction occurring in the SOFC decreases. Therefore, a relatively high proportion of the hydrogen and air entering the cell remains unreacted. When the unreacted hydrogen and oxygen reach the combustion chamber, they burn, generating additional heat. This exothermic reaction greatly increases the temperature of the exhaust gas. Then it is fed back to the preheater, increasing the temperature of the air and fuel in the preheater.

[0152] Set the load current to 100A, the inlet temperatures of air and fuel are both 275K, and the fuel flow rate suddenly changes from 1×10-3mol / s to 2×10 -3 mol / s at 7000 seconds to observe the dynamic response of the system. When the fuel flow rate suddenly changes, for a single fuel cell stack, due to the increase in the flow rate enhancing the convective heat transfer in the stack, the temperature of the stack will gradually decrease, but due to the addition of the combustion and preheating subsystems, more unreacted fuel enters the combustion chamber for combustion, and the temperature of the combustion gas increases. Then, under the action of the two preheaters, the temperature of the gas entering the stack increases. Figure 12 and Figure 13Shows the dynamic responses of each fluid in the preheater after mutation, with the abscissa being time and the ordinate being temperature T or voltage. In the first preheater, the outlet temperatures of air and fuel rise to 621K and 691K respectively, and then flow out of the second preheater at temperatures and voltages of 872K and 959K respectively.

[0153] In addition, similar dynamic simulations and performance optimizations can be carried out on the system under different operating conditions to evaluate the performance of the system under different fuel flow rates, ambient temperatures, and load conditions. Through the dynamic simulation model, by adjusting relevant input parameters (such as fuel flow rate, ambient temperature, and load changes), different operating scenarios can be simulated, and key parameters such as the temperature of the fuel cell stack, active and reactive power output can be analyzed. Based on the simulation results, key parameters of the system, such as thermal resistance, thermal capacitance, electrode materials, and control parameters of power electronic devices, are optimized to improve the overall efficiency of the system. Under different distributed generation scenarios, by adjusting the input parameters in the dynamic simulation model, different external environments and operating conditions can be simulated, including but not limited to ambient temperature changes, load fluctuations, and fuel supply conditions, etc. In this way, the response characteristics of the system under various conditions are evaluated, and the operating strategy of the grid-connected fuel cell power generation system is optimized accordingly to ensure that the system can operate efficiently under various conditions. For example, by using the dynamic simulation model to optimize the settings of fuel flow rate and current density, stable output of the system can be ensured under different load conditions. Based on the simulation analysis results, an intelligent control strategy is formulated, including real-time adjustment of the output power of the fuel cell, optimization of the system's energy distribution scheme, and rapid response and adjustment when the system shows abnormalities, to ensure the safe, stable, and efficient operation of the distributed generation system. The intelligent control system can dynamically adjust the system operating parameters, such as optimizing fuel utilization and energy conversion efficiency by adjusting fuel supply. In addition, the dynamic simulation model of the present application can also be used for fault prediction and diagnosis. By real-time monitoring of key system parameters (such as voltage, current, temperature, etc.), abnormal changes are identified and maintenance measures are taken in advance. The dynamic simulation model can simulate different types of faults (such as thermal management faults, electrochemical faults, power electronic faults), thereby formulating corresponding diagnostic strategies and providing fault location and repair suggestions.

[0154] The dynamic simulation model of the present application also provides an implementation method for the comprehensive optimization of various distributed energy systems, significantly improving the overall energy utilization efficiency. By integrating the overall standard circuit model of the fuel cell with other energy system models such as photovoltaic and wind energy, comprehensive simulation analysis can be carried out to evaluate the dynamic response characteristics of the system under different energy combinations. Based on these simulation results, a comprehensive energy management system (EMS) is developed to optimize the coordinated operation of each energy system and improve the overall energy utilization efficiency. Combining with a battery energy storage system, through the regulation of energy storage devices, load balancing and power quality management are realized, further optimizing the dynamic response and stability of the multi-energy system.

[0155] In an exemplary embodiment, the present application further provides a dynamic simulation device for a network-forming fuel cell power generation system, including: a system overall structure and configuration determination module for determining the overall structure and configuration of the network-forming fuel cell power generation system; a comprehensive modeling module for performing comprehensive modeling based on the overall structure and configuration by using the thermoelectric analogy method to establish an overall standard circuit model of the network-forming fuel cell power generation system; a dynamic simulation model construction module for constructing a dynamic simulation model of the network-forming fuel cell power generation system based on the overall standard circuit model; a dynamic response simulation module for simulating the dynamic response of the network-forming fuel cell power generation system under different distributed power generation scenarios by adjusting the input parameters of the dynamic simulation model to obtain simulation results; and a cluster analysis module for performing cluster analysis on the simulation results under different distributed power generation scenarios to formulate an intelligent control strategy for the distributed power generation system.

[0156] In an exemplary embodiment, the present application further provides a computer device, which may be a server or a terminal. The computer device includes a processor, a memory, an input / output interface, and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by the processor, implements the dynamic simulation method of the network-forming fuel cell power generation system.

[0157] In an exemplary embodiment, the present application further provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by the processor, implements the dynamic simulation method of the network-forming fuel cell power generation system.

[0158] In an exemplary embodiment, the present application further provides a computer program product, including a computer program, and the computer program, when executed by the processor, implements the dynamic simulation method of the network-forming fuel cell power generation system.

[0159] Those of ordinary skill in the art can understand that all or part of the processes in the above-described embodiment methods can be completed by hardware related to computer program instructions. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory or other medium provided in the embodiments of the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. 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.

[0160] It should be noted that the information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0161] The method of this application is applicable to the dynamic modeling, clustering technology and characteristic analysis of grid-connected fuel cell power generation systems, and is not limited to a specific object or system. First, select the configuration and structure of the grid-connected fuel cell power generation system and conduct architecture design, including fuel cell stacks, preheating subsystems, tail gas recovery subsystems, and power electronic devices, etc. During the design process, special consideration is given to its integration and compatibility in distributed power generation systems to ensure that the system can operate in coordination with other distributed energy systems. The goal of this step is to determine the overall structure and configuration of the system, providing a basis for subsequent modeling and simulation. According to the structure of the selected object, clarify its operation process and mechanism, and obtain its characteristic parameters including but not limited to those characterizing its heat and mass transfer, electrochemical reaction, and power electronic characteristics, such as fuel type, flow rate, temperature, pressure, conductivity, etc. The acquisition of these parameters is for accurate mathematical modeling and simulation analysis in subsequent steps. By obtaining these parameters, it is ensured that the model can accurately reflect the dynamic behavior of the system under different operating conditions. With the obtained characteristic parameters and system operation mechanism as a reference, clarify the formulas and mathematical models that describe the dynamic change laws of each component and each process. For the convenience of modeling, the formulas and mathematical models are divided into a thermal part standard impedance model, an electrochemical part standard impedance model, and a power electronic part according to the parameter types they describe, and at the same time consider how these models interact with other distributed energy devices in the distributed power generation system to achieve the overall optimization of the system.

[0162] To accurately describe the thermoelectric coupling characteristics of the fuel cell system, it is first necessary to perform circuit equivalent processing on each thermal process in the system. This application identifies and analyzes the preheating process, electrochemical heat generation, and heat transfer process between components in the system, and uses the thermoelectric analogy method to convert these thermal processes into corresponding circuit elements. The heat conduction and heat convection processes are converted into resistance elements, the heat capacity is converted into a capacitance element, and the heat generation process is equivalent to a power supply element. Through the combination of these circuit elements, a thermal part standard impedance model is constructed to simulate the heat transfer path and temperature distribution in the system. This thermal part standard impedance model provides a necessary basis for the integration of the electrochemical and power electronic parts in subsequent steps, ensuring that the thermoelectric coupling relationship in the overall model of the system can be accurately simulated and optimized.

[0163] After completing the equivalent circuit model of the thermal process, the standard impedance model of the electrochemical part of the fuel cell system was then established to accurately describe the electrochemical reaction process within the fuel cell. In this step, key electrochemical processes such as electrode reactions, electron conduction, and ion conduction were identified and transformed into circuit elements. The open-circuit voltage source represents the initial voltage of the fuel cell, the polarization resistance and concentration resistance respectively describe the voltage losses caused by electrode reactions and concentration gradients, while the double-layer capacitance simulates the capacitance characteristics of the electrode surface. Through the combination of these elements, a complete standard impedance model of the electrochemical part was constructed. The construction of this model is not only closely related to the thermoelectric model but also provides a solid foundation for subsequent model integration. By integrating the standard impedance model of the thermal part and the standard impedance model of the electrochemical part, a unified overall standard dynamic impedance model can be formed to comprehensively simulate the performance of the fuel cell system under different distributed generation scenarios, thereby providing a scientific basis for system optimization and the formulation of control strategies.

[0164] According to the configuration and structure of the grid-connected fuel cell, the circuit diagram of the power electronics part was determined, including DC / DC converters, three-phase DC / AC inverters, etc. Since the temperature change in the thermal part determines the output voltage of the electrochemical part, and the output voltage of the fuel cell determines the input parameters of the DC-DC converter, integrating the circuit model of the power electronics part with the standard impedance models of the thermal part and the electrochemical part can make the parameters and dynamic responses between the various parts match each other, ensuring the seamless connection of the model and guaranteeing the efficient interaction and stable operation of the fuel cell system with other distributed energy devices.

[0165] Based on the constructed overall standard circuit model, according to the complexity of the equations therein, using a programming language capable of describing the equations, the equations were entered to make the various parts connect to form a dynamic simulation model of the grid-connected fuel cell power generation system. The necessary characteristic parameters that have been determined were input into the model, and after setting the initial operating conditions, simulation was carried out. By comparing the simulation results with the actual operating data of the object, the errors existing in each part were found, the sources of the errors were analyzed, and then the parameters in the model were adjusted until the errors were within an acceptable range, after which the modeling was completed.

[0166] Based on the completed dynamic simulation model, the overall operation regulation and analysis of a large-scale distributed generation system were carried out. Through this dynamic simulation model, the configuration and operating parameters of each grid-connected fuel cell unit in the system can be optimized to achieve load balancing, energy management, and fault diagnosis of the entire distributed generation system, thereby improving the overall stability and operating efficiency of the system.

[0167] Under different distributed generation scenarios, by adjusting the input parameters in the dynamic simulation model, different external environments and operating conditions are simulated, including but not limited to environmental temperature changes, load fluctuations, and fuel supply conditions, etc. According to the simulation results, the operating strategy of the grid-connected fuel cell power generation system is optimized to ensure the efficient operation of the system under various conditions.

[0168] Based on the simulation analysis results, intelligent control strategies for the distributed generation system can also be formulated, including real-time adjustment of the output power of the fuel cell, optimization of the system's energy distribution scheme, and rapid response and adjustment when the system experiences abnormalities, ensuring the safe, stable, and efficient operation of the distributed generation system. According to the simulation results, the temperature change of the fuel cell stack can also be monitored to evaluate the effectiveness of the thermal management system. Analyze the active and reactive power output from the fuel cell to the grid to evaluate the performance of power electronic devices and the stability of the power grid.

[0169] This application can guide users to perform dynamic simulation using the model and evaluate the performance of the system under different fuel flow rates, environmental temperatures, and load conditions. According to the simulation results, key parameters of the system are optimized, such as thermal resistance, thermal capacitance, electrode materials, and control parameters of power electronic devices, to improve the overall efficiency of the system. Further, by integrating the fuel cell model with other energy system models such as photovoltaic and wind energy, comprehensive simulation analysis is carried out, and an integrated energy management system can also be developed to optimize the coordinated operation of each energy system and improve the overall energy utilization efficiency. Or develop intelligent control algorithms to dynamically adjust the system operating parameters and optimize the fuel utilization rate and energy conversion efficiency.

[0170] It can be seen that the method of this application can perform accurate dynamic modeling for the grid-connected fuel cell power generation system and comprehensively describe its response characteristics. Based on this dynamic simulation model, the comprehensive performance of the power generation system can be deeply analyzed, helping users to optimize the operation plan on this basis and improve the system efficiency and stability. The main innovative achievements of this application are summarized as follows: 1) The method proposed in this application can fully describe the thermoelectric coupling characteristics of the grid-forming fuel cell power generation system, accurately reflect the synchronous dynamic changes of thermoelectric parameters, and provide a theoretical basis for the efficient operation of the system; 2) The method of this application can synchronously analyze the output performance of the thermoelectric parameters of the system from multiple time scales to ensure a comprehensive evaluation of the short-term and long-term dynamic behaviors of the system; 3) The dynamic simulation model proposed in this application can simulate the changes of parameters such as the output voltage, current of the fuel cell power generation system under different operating conditions, and the active and reactive power transmitted to the grid, providing a scientific basis for the operation and maintenance personnel to formulate accurate operation and control strategies. Especially in the distributed generation system, it can significantly improve the efficiency and reliability of the overall energy management and has a wide range of application prospects.

[0171] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, 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, it should be considered as the scope recorded in this specification.

[0172] In this article, specific examples are used to elaborate on the principles and implementation manners of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A dynamic simulation method for a grid-type fuel cell power generation system, characterized in that: include: Determine the overall structure and configuration of the grid-type fuel cell power generation system; Based on the overall structure and configuration, a comprehensive modeling is performed using a thermoelectric analogy method to establish an overall standard circuit model of a grid-type fuel cell power generation system; Constructing a dynamic simulation model of a grid-type fuel cell power generation system based on the overall standard circuit model; By adjusting the input parameters of the dynamic simulation model, the dynamic response of the grid-type fuel cell power generation system in different distributed power generation scenarios is simulated to obtain simulation results. Cluster analysis is performed on the simulation results under different distributed generation scenarios to formulate intelligent control strategies for distributed generation systems.

2. The dynamic simulation method of a grid-type fuel cell power generation system according to claim 1, characterized in that: The overall structure and configuration of the determined grid-type fuel cell power generation system specifically include: The overall structure and configuration of the grid-type fuel cell power generation system include a solid oxide fuel cell system and power electronic equipment; the solid oxide fuel cell system includes multiple parallel SOFC arrays; each SOFC array is composed of multiple SOFC stack systems; each SOFC stack system includes a fuel cell stack, a preheating subsystem and a tail gas recovery subsystem; wherein the preheating subsystem is composed of two preheaters; the tail gas recovery subsystem includes a tail gas burner, a tail gas duct, an air duct and a fuel duct; the power electronic equipment includes a DC / DC converter and a DC / AC inverter for adjusting the output voltage of the fuel cell.

3. The dynamic simulation method of a grid-type fuel cell power generation system according to claim 2, characterized in that: Based on the overall structure and configuration, a comprehensive modeling is performed using the thermoelectric analogy method to establish an overall standard circuit model of a grid-type fuel cell power generation system, specifically including: The thermal part of the SOFC stack system is modeled using the thermoelectric analogy method, and a standard impedance model of the thermal part is established; The electrochemical part of the SOFC stack system is modeled using the thermoelectric analogy method, and a standard impedance model of the electrochemical part is established; The standard impedance model of the thermal part and the standard impedance model of the electrochemical part are transformed into the standard impedance model of the electrochemical part by the stack temperature T cell Connect and couple to form an overall standard dynamic impedance model of the SOFC stack system; The circuit model of the power electronic device is combined with the overall standard dynamic impedance model of multiple SOFC stack systems through the SOFC array output voltage V out The connections are made to establish an overall standard circuit model of a grid-type fuel cell power generation system.

4. The dynamic simulation method of a grid-type fuel cell power generation system according to claim 3, characterized in that: The method of using the thermoelectric analogy method to model the thermal part of the SOFC stack system and establish a standard impedance model of the thermal part specifically includes: Using the thermoelectric analogy method, the convection heat transfer process in the SOFC stack system is converted into a resistance element, the heat capacity is converted into a capacitance element, and the electrochemical heat generation process is equivalent to a power supply element. Through the combination of resistance elements, capacitance elements and power elements, a standard impedance model of the thermal part is constructed to simulate the heat transfer path and temperature distribution in the SOFC stack system.

5. The dynamic simulation method of a grid-type fuel cell power generation system according to claim 4, characterized in that: The method of using thermoelectric analogy to model the electrochemical part of the SOFC stack system and establish a standard impedance model of the electrochemical part specifically includes: According to the electrochemical reaction, polarization loss, ohmic loss and concentration loss in the SOFC stack system, the stack output voltage V is established. cell The mathematical equation V cell =E cell -V act -V ohm -V conc ; where E cell is the corresponding Nernst potential; V act is the activation voltage drop; V ohm is the ohmic voltage drop; V conc is the concentration voltage drop; According to the electrical output characteristics of the SOFC stack system, the SOFC array output voltage V is established. out The mathematical equation V out =N cell V cell ; where V cell is the stack output voltage; N cell is the number of plates.

6. The dynamic simulation method of a grid-type fuel cell power generation system according to claim 5, characterized in that: The dynamic response of the grid-type fuel cell power generation system in different distributed power generation scenarios is simulated by adjusting the input parameters of the dynamic simulation model to obtain simulation results, which specifically include: By changing the fuel and air flow, ambient temperature and load current at the beginning of the simulation, the dynamic response of the grid-type fuel cell power generation system in different distributed power generation scenarios is simulated, and the thermoelectric output parameter V of the SOFC stack system is obtained. out and T cell And the power P output from the SOFC array to the grid out The dynamic change law over time is taken as the simulation result; among which P out =V out ·I; I is the load current.

7. The dynamic simulation method of a grid-type fuel cell power generation system according to claim 6, characterized in that: The cluster analysis of simulation results under different distributed generation scenarios and the formulation of intelligent control strategies for distributed generation systems specifically include: Based on the control strategy P out (t) = P ref +K p ·(P set (t)-P out (t)), dynamically adjust the output power P at time t according to the grid demand out (t); where P ref is the reference power; K p is the proportional gain; P set (t) is the set power at time t; When an abnormality occurs in the system, the control strategy based on the abnormal situation Determine the power adjustment ΔP at time t out (t); where K d is the differential gain; E errot (t) is the energy error at time t; The power adjustment ΔP based on time t out (t) Adjust the output power P at time t out (t).

8. A dynamic simulation device for a grid-type fuel cell power generation system, characterized in that: include: A system overall structure and configuration determination module, used to determine the overall structure and configuration of a grid-type fuel cell power generation system; A comprehensive modeling module, used to perform comprehensive modeling based on the overall structure and configuration using a thermoelectric analogy method to establish an overall standard circuit model of a grid-type fuel cell power generation system; A dynamic simulation model building module, used to build a dynamic simulation model of a grid-type fuel cell power generation system based on the overall standard circuit model; The dynamic response simulation module is used to simulate the dynamic response of the grid-type fuel cell power generation system in different distributed power generation scenarios by adjusting the input parameters of the dynamic simulation model to obtain simulation results; The cluster analysis module is used to perform cluster analysis on the simulation results under different distributed power generation scenarios and formulate intelligent control strategies for distributed power generation systems.

9. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the dynamic simulation method of the grid-type fuel cell power generation system described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the dynamic simulation method of the grid-type fuel cell power generation system described in any one of claims 1 to 7 is implemented.

11. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the dynamic simulation method of the grid-type fuel cell power generation system described in any one of claims 1 to 7 is implemented.

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