Multi-energy complementary oriented natural gas SOFC intelligent charging system

Abstract

The application discloses a kind of natural gas SOFC intelligent charging systems for multi-energy complementary, it is related to intelligent microgrid technical field, including, data acquisition module, energy management unit executes self-checking procedure and collects multi-source data, based on the real-time data of acquisition, energy management unit obtains benchmark instruction using multi-objective optimization scheduling model solution;Hybrid module, before benchmark instruction execution, through impedance analysis module on-line measurement electrochemical impedance spectroscopy, impedance spectroscopy characteristic is solved to obtain health state coefficient, and the optimal fuel mixing ratio of dynamic decision-making;Execution module, based on the instruction of optimization adjustment, energy management unit will control instruction issue to each unit and execute the application is updated by real-time feedback data rolling optimization model;Transmission module executes orderly shutdown and uploads data when shutdown;Optimization module formulates optimal start strategy based on digital twin model when restarting, completes system restart.

Description

Technical Field

[0001] This invention relates to the field of smart microgrid technology, and in particular to a smart charging system for natural gas SOFCs with multi-energy complementarity. Background Technology

[0002] With the deepening of energy transition, solid oxide fuel cells (SOFCs) are increasingly widely used in the field of distributed energy due to their advantages such as high power generation efficiency, flexible fuel, and low emissions. Domestic and foreign companies have achieved commercial application of SOFC systems ranging from kilowatts to megawatts, with combined heat and power efficiency reaching over 90%, providing reliable clean power solutions for scenarios such as industrial parks and data centers.

[0003] When SOFC systems are combined with highly volatile electric vehicle charging loads, existing technologies face significant challenges. Electric vehicle charging demands are characterized by rapid and random fluctuations, while SOFCs, limited by their electrochemical reaction kinetics, have slow power regulation responses, typically on the order of minutes. This mismatch between source and load dynamics makes it difficult for the system to maintain real-time power balance on a timescale of seconds, affecting power quality and system stability. Existing systems lack real-time perception and integration of the SOFC's health status, and fuel control strategies are often static, failing to achieve an optimal balance between flexible operation and equipment lifespan. Furthermore, the system shutdown and restart processes lack refined energy management. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a smart charging system for natural gas SOFCs that addresses the contradiction between the slow dynamic response characteristics of SOFCs and the rapid fluctuation characteristics of electric vehicle charging loads in real time, and realizes fuel optimization and refined energy management based on SOFC health status perception.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: This invention provides a smart charging system for natural gas SOFCs with multi-energy complementarity, comprising a data acquisition module, an energy management unit that executes a self-test program and acquires multi-source data, and an energy management unit that uses a multi-objective optimization scheduling model to obtain a baseline command based on the acquired real-time data. Before the baseline command is executed, the mixing module measures the electrochemical impedance spectrum online through the impedance analysis module, analyzes the impedance spectrum characteristics to calculate the health state coefficient, and dynamically decides the optimal fuel mixing ratio. The execution module, based on the optimized and adjusted instructions, sends control instructions to each unit for execution by the energy management unit; The control module and energy management unit dynamically aggregate electric vehicles that support V2G functionality into an equivalent virtual energy storage stack, compare power differences in real time, and prioritize the use of the virtual energy stack for second-level power balance control. The update module feeds back the actual operating data to the energy management unit in real time and performs rolling updates to the multi-objective optimization scheduling model. When the transmission module is shut down, the energy management unit gradually reduces the SOFC power and simultaneously increases energy storage or switches to grid power. After the shutdown, the operating data is uploaded to the cloud platform. The optimization module, upon restart, the energy management unit executes a self-test procedure and formulates the optimal startup strategy, and after startup is complete, re-enters the data acquisition phase.

[0007] As a preferred embodiment of the intelligent charging system for multi-energy complementary natural gas SOFCs described in this invention, the energy management unit executes a self-test procedure and collects multi-source data, including the following steps: The energy management unit collects the output power of the photovoltaic array and wind turbine, the real-time electricity price of the grid, the state of charge of the energy storage battery, the connection status of each charging pile, and the operating parameters of the solid oxide fuel cell (SOFC) stack. The energy management unit initiates self-test procedures for the valves, flow meters, and pressure sensors of the natural gas supply unit; the voltage, temperature, and sealing of the solid oxide fuel cell (SOFC) stack; the circuit breaker and converter status of the power electronic equipment; and the photovoltaic inverter, wind power converter, grid connection point, and energy storage battery BMS of the multi-energy interface. The self-test procedure of the energy management unit confirms the status of valves, flow meters, and pressure sensors in the natural gas supply unit, the voltage, temperature, and sealing of the solid oxide fuel cell (SOFC) stack, the status of circuit breakers and converters in the power electronic equipment, and that the photovoltaic inverters, wind power converters, grid connection points, and energy storage battery BMS of the multi-energy interface are in normal working condition.

[0008] As a preferred embodiment of the intelligent charging system for multi-energy complementary natural gas SOFCs described in this invention, the energy management unit obtains the baseline command by using a multi-objective optimization scheduling model based on the collected real-time data, including the following steps: Based on the collected output power of photovoltaic arrays and wind turbines, real-time grid electricity prices, state of charge of energy storage batteries, connection status of each charging pile, and operating parameters of solid oxide fuel cell (SOFC) stacks, a multi-objective optimization scheduling model is established with the goal of minimizing the total system operating cost. The multi-objective optimization scheduling model includes natural gas cost, electricity purchase cost, electricity sales revenue, and carbon trading revenue. The multi-objective optimization scheduling model is input with the collected output power of photovoltaic arrays and wind turbines, real-time grid electricity price, state of charge of energy storage batteries, connection status of each charging pile, and operating parameters of solid oxide fuel cell (SOFC) stacks. It also sets the health status constraints of solid oxide fuel cells (SOFC), the charge and discharge depth constraints of energy storage batteries, the power exchange limit constraints of the grid, and the power demand constraints of each charging pile. Under the conditions of satisfying the health state constraints of solid oxide fuel cells (SOFC), the charge and discharge depth constraints of energy storage batteries, the power exchange limit constraints of the power grid, and the power demand constraints of each charging pile, a multi-objective optimization scheduling model with the goal of minimizing the total system operating cost is solved to obtain the baseline power setpoint of solid oxide fuel cells (SOFC), the charge and discharge power plan of energy storage batteries, the power exchange plan with the power grid, and the baseline instructions for the power allocation plan of each charging pile in the next 15-minute period.

[0009] As a preferred embodiment of the intelligent charging system for multi-energy complementary natural gas SOFCs described in this invention, the following steps are included: Before the execution of the reference command, the electrochemical impedance spectroscopy is measured online through an impedance analysis module, and the health state coefficient is obtained by analyzing the impedance spectroscopy characteristics: Before the execution of the baseline instructions for the reference power setting of the solid oxide fuel cell (SOFC), the charging and discharging power plan of the energy storage battery, the power exchange plan with the grid, and the power allocation plan of each charging pile in the next 15-minute period, the impedance analysis module injects a small signal disturbance of a specific frequency into the solid oxide fuel cell (SOFC) stack. The impedance analysis module measures the electrochemical impedance spectrum of a solid oxide fuel cell (SOFC) stack online by injecting a small signal perturbation of a specific frequency into the SOFC stack. The energy management unit analyzes the electrochemical impedance spectroscopy characteristics of the solid oxide fuel cell (SOFC) stack based on the online measurement of the electrochemical impedance spectroscopy by the impedance analysis module. Based on the electrochemical impedance spectral characteristics of the analyzed solid oxide fuel cell (SOFC) stack, the energy management unit calculates the health status coefficient, which reflects the internal electrochemical reaction activity and aging degree of the solid oxide fuel cell (SOFC) stack.

[0010] As a preferred embodiment of the intelligent charging system for multi-energy complementary natural gas SOFCs described in this invention, the dynamic decision-making of the optimal fuel mixing ratio includes the following steps: The energy management unit acquires the green hydrogen flow rate produced by the water electrolysis hydrogen production unit, the reference power setting value of the solid oxide fuel cell (SOFC) in the next 15-minute cycle, and the health status coefficient reflecting the electrochemical reaction activity and aging degree inside the solid oxide fuel cell (SOFC) stack. The green hydrogen flow rate produced by the water electrolysis hydrogen production unit, the baseline power setting value of the solid oxide fuel cell (SOFC) in the next 15-minute cycle, and the health status coefficient reflecting the electrochemical reaction activity and aging degree inside the solid oxide fuel cell (SOFC) stack are input into the fuel mixing decision function. By using the fuel mixing decision function, the dynamic optimal mixing ratio of natural gas and green hydrogen in solid oxide fuel cell (SOFC) fuel is obtained.

[0011] As a preferred embodiment of the intelligent charging system for multi-energy complementary natural gas SOFCs described in this invention, the energy management unit, based on optimized and adjusted instructions, issues control instructions to each unit for execution, including the following steps: Based on the dynamic optimal mixing ratio of natural gas and green hydrogen in the solid oxide fuel cell (SOFC) fuel, the energy management unit optimizes and adjusts the baseline power setpoint of the SOFC, the charging and discharging power plan of the energy storage battery, the power exchange plan with the grid, and the power allocation plan of each charging pile within the next 15-minute period obtained by the energy management unit. This generates optimized instructions that include the target power and fuel command of the SOFC, the charging and discharging power command of the energy storage battery, the power exchange command with the grid, and the power allocation command of each charging pile. The optimized target power and fuel command for the solid oxide fuel cell (SOFC) in the optimized and adjusted instructions are sent to the local controller of the solid oxide fuel cell (SOFC); The optimized charging and discharging power command of the energy storage battery in the optimized and adjusted command is sent to the energy storage converter, and the optimized power exchange command with the grid in the optimized and adjusted command is sent to the grid-connected inverter. The optimized power allocation instructions for each charging pile are sent to each charging pile controller. The local controller of the solid oxide fuel cell (SOFC), the energy storage converter, the grid-connected inverter, and each charging pile controller receive and execute the optimized instructions.

[0012] As a preferred embodiment of the multi-energy complementary natural gas SOFC smart charging system described in this invention, the energy management unit dynamically aggregates electric vehicles supporting V2G functionality into an equivalent virtual energy storage stack, compares power differences in real time, and prioritizes the use of the virtual stack for second-level power balance control, including the following steps: The energy management unit obtains the connection status of each currently connected charging pile, identifies electric vehicles that support V2G functionality from the connection status of each charging pile, and obtains the remaining battery capacity, charging and discharging power limits, and the minimum charge level set by the owner for leaving the site from the identified electric vehicles that support V2G functionality. Based on the acquired remaining battery capacity, charging and discharging power limits, and the minimum charge level set by the vehicle owner for leaving the site, the energy management unit dynamically aggregates all currently connected electric vehicles that support V2G functionality into an equivalent virtual energy storage stack that can be quickly charged and discharged. During the execution of optimized and adjusted instructions by the local controller of solid oxide fuel cell (SOFC), energy storage converter, grid-connected inverter, and each charging pile controller, the actual output of solid oxide fuel cell (SOFC), the actual output of photovoltaic array and wind turbine, and the total charging demand including electric vehicles with V2G function and electric vehicles without V2G function are collected. Based on the real-time collected actual output of solid oxide fuel cells (SOFC), the actual output of photovoltaic arrays and wind turbines, and the total charging demand including electric vehicles with and without V2G functionality, the power difference between the actual output of solid oxide fuel cells (SOFC) and the sum of the actual output of photovoltaic arrays and wind turbines and the total charging demand including electric vehicles with and without V2G functionality is calculated and compared. When the calculated power difference exceeds a set threshold (e.g., 1kW), the equivalent virtual energy storage stack that can be quickly charged and discharged is invoked first through dynamic aggregation. Within a few hundred milliseconds, a discharge or charge command is sent to the equivalent virtual energy storage stack that can be quickly charged and discharged, thereby achieving second-level power balance control.

[0013] As a preferred embodiment of the intelligent charging system for multi-energy complementary natural gas SOFCs described in this invention, the following steps are included: Real-time feedback of actual operating data to the energy management unit, and continuous updating of the multi-objective optimization scheduling model: Each execution unit generates actual operating data during the execution of instructions, including the actual output of solid oxide fuel cells (SOFC), the actual charging and discharging power of energy storage batteries, the actual exchange power with the grid, the actual charging power of each charging pile, and the actual response of the dynamically aggregated equivalent virtual energy storage stack that can be charged and discharged quickly. Each execution unit feeds back the actual operating data to the energy management unit in real time, receives the actual operating data, and obtains the latest output forecasts of ultra-short-term photovoltaic arrays and wind turbines, the latest ultra-short-term charging load forecasts, and the latest real-time electricity price data. Based on actual operating data, combined with the latest output forecasts for ultra-short-term photovoltaic arrays and wind turbines, the latest ultra-short-term charging load forecasts, and the latest real-time electricity price data, the multi-objective optimization scheduling model is continuously updated to generate the baseline instructions for the next calculation cycle.

[0014] As a preferred embodiment of the intelligent charging system for natural gas SOFCs oriented towards multi-energy complementarity described in this invention, the system includes the following steps: When shutting down, the energy management unit gradually reduces the SOFC power and simultaneously increases energy storage or switches to grid power. After shutdown, the operating data is uploaded to the cloud platform. The energy management unit receives a shutdown command and issues a command to gradually reduce the actual output of the solid oxide fuel cell (SOFC), while simultaneously issuing a command to increase the discharge power of the energy storage battery or increase the power purchased from the grid. After the actual output of a solid oxide fuel cell (SOFC) drops to the minimum set value, the energy management unit controls the remaining fuel and waste heat to complete the cooling cycle of the SOFC stack. The energy management unit uploads the actual output of the solid oxide fuel cell (SOFC), the actual output of the photovoltaic array and wind turbine, the actual charging and discharging power of the energy storage battery, the actual power exchanged with the grid, the actual charging power of each charging pile, the actual response of the dynamically aggregated equivalent virtual energy storage stack that can be charged and discharged quickly, and the operating performance data of the solid oxide fuel cell (SOFC) stack to the cloud platform.

[0015] As a preferred embodiment of the intelligent charging system for multi-energy complementary natural gas SOFCs described in this invention, the energy management unit executes a self-test procedure and formulates an optimal startup strategy upon restart. After startup, it re-enters the data acquisition phase, including the following steps: Based on the operating performance data of solid oxide fuel cell (SOFC) stacks uploaded to the cloud platform, the optimal start-up temperature rise curve of solid oxide fuel cell (SOFC) stacks is analyzed and predicted through a digital twin model of solid oxide fuel cell (SOFC) performance degradation. Based on the optimal start-up temperature rise curve of the solid oxide fuel cell (SOFC) stack predicted by the digital twin model of solid oxide fuel cell (SOFC) performance degradation, the optimal start-up strategy of the solid oxide fuel cell (SOFC) stack is formulated. Based on the optimal start-up strategy of the solid oxide fuel cell (SOFC) stack, control commands are sent to the start-up burner or external electric heater to initiate programmed heating of the natural gas reformer and the solid oxide fuel cell (SOFC) stack. After the solid oxide fuel cell (SOFC) stack completes programmed temperature rise and start-up, it re-enters the stage of collecting data on the output power of the photovoltaic array and wind turbine, real-time grid electricity price, state of charge of the energy storage battery, connection status of each charging pile, and operating parameters of the solid oxide fuel cell (SOFC) stack.

[0016] The beneficial effects of this invention are as follows: The data acquisition module collects multi-source data and performs self-checks, then uses a multi-objective optimization scheduling model to solve for the baseline command; before command execution, the mixing module measures the electrochemical impedance spectroscopy online through impedance analysis and calculates the health state coefficient, dynamically deciding on the optimal fuel mixing ratio; the execution module controls the operation of each unit based on the optimized command; the control module dynamically aggregates V2G electric vehicles into a virtual energy storage stack, achieving second-level power balance; the update module continuously updates the optimization model through real-time feedback data; the transmission module performs orderly shutdown and uploads data during shutdown; and the optimization module formulates the optimal startup strategy based on the digital twin model during restart, completing the system restart. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart for a smart charging system for natural gas SOFCs oriented towards multi-energy complementarity. Detailed Implementation

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0020] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0021] Secondly, the term "one embodiment" or "example" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the invention. The appearance of an embodiment in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that mutually excludes other embodiments.

[0022] Reference Figure 1This is one embodiment of the present invention, which provides a smart charging system for natural gas SOFCs with multi-energy complementarity, comprising the following steps: The data acquisition module and the energy management unit execute a self-test procedure and collect data from multiple sources. Based on the collected real-time data, the energy management unit uses a multi-objective optimization scheduling model to solve for the baseline command.

[0023] The energy management unit collects the output power of the photovoltaic array and wind turbine, the real-time electricity price of the grid, the state of charge of the energy storage battery, the connection status of each charging pile, and the operating parameters of the solid oxide fuel cell (SOFC) stack.

[0024] Furthermore, the energy management unit obtains real-time output power of the photovoltaic array from the photovoltaic inverter, real-time output power of the wind turbine from the wind power converter, real-time grid electricity price data from the grid dispatch system, state of charge of the energy storage battery from the energy storage battery management system, connection status of the charging piles from each charging pile controller, and operating parameters of the solid oxide fuel cell stack from the local controller of the solid oxide fuel cell stack, including stack voltage, current, temperature, fuel flow, reformer temperature, and exhaust gas composition, thus completing the real-time acquisition of multi-source data.

[0025] The energy management unit initiates self-test procedures for the valves, flow meters, and pressure sensors of the natural gas supply unit; the voltage, temperature, and sealing of the solid oxide fuel cell (SOFC) stack; the circuit breakers and converters of the power electronic equipment; and the photovoltaic inverters, wind power converters, grid connection points, and energy storage battery BMS of the multi-energy interface.

[0026] Furthermore, the energy management unit sends status query commands to the natural gas supply unit to obtain the valve opening degree, flow meter reading, and pressure sensor data of the natural gas supply unit; it sends status query commands to the solid oxide fuel cell stack to obtain the voltage, temperature, and sealing test results of the stack; it sends status query commands to the power electronic equipment to obtain the circuit breaker opening and closing status and the converter operating status; and it sends status query commands to the multi-energy interface to obtain the operating status and alarm information of the photovoltaic inverter, wind power converter, grid connection point, and energy storage battery BMS, and initiates the status self-check program for the above-mentioned equipment.

[0027] The self-test procedure of the energy management unit confirms the status of valves, flow meters, and pressure sensors in the natural gas supply unit, the voltage, temperature, and sealing of the solid oxide fuel cell (SOFC) stack, the status of circuit breakers and converters in the power electronic equipment, and that the photovoltaic inverters, wind power converters, grid connection points, and energy storage battery BMS of the multi-energy interface are in normal working condition.

[0028] Furthermore, the energy management unit performs logical judgments on the valve opening, flow meter readings, and pressure sensor data acquired from the natural gas supply unit to confirm that the valve opening is within the allowable range, the flow meter reading is normal, and the pressure sensor data is within the set threshold. It also judges the voltage, temperature, and sealing test results of the solid oxide fuel cell stack to confirm that the voltage is within the rated range, the temperature is within the set range, and the sealing is good. It judges the circuit breaker opening and closing status and converter operating status of the power electronic equipment to confirm that the circuit breaker is in the closed state and the converter is operating normally. Finally, it judges the operating status and alarm information of the multi-energy interface photovoltaic inverter, wind power converter, grid connection point, and energy storage battery BMS to confirm that there are no alarm information and the equipment is in normal working condition, thus completing the self-test procedure confirmation.

[0029] Based on the collected data on the output power of photovoltaic arrays and wind turbines, real-time grid electricity prices, state of charge of energy storage batteries, connection status of each charging pile, and operating parameters of solid oxide fuel cell (SOFC) stacks, a multi-objective optimization scheduling model is established with the goal of minimizing the total system operating cost. The multi-objective optimization scheduling model includes natural gas cost, electricity purchase cost, electricity sales revenue, and carbon trading revenue.

[0030] Furthermore, the energy management unit uses the collected photovoltaic array output power, wind turbine output power, real-time grid electricity price, energy storage battery state of charge, connection status of each charging pile, and solid oxide fuel cell stack operating parameters as input variables to construct a multi-objective optimization scheduling model with the objective function of minimizing the total system operating cost. The objective function consists of four sub-objectives: natural gas cost, electricity purchase cost, electricity sales revenue, and carbon trading revenue. Among them, natural gas cost is the cost generated by the solid oxide fuel cell stack consuming natural gas, electricity purchase cost is the cost generated by purchasing electricity from the grid, electricity sales revenue is the revenue generated by selling electricity to the grid, and carbon trading revenue is the carbon trading revenue obtained by reducing carbon emissions through clean power generation.

[0031] The multi-objective optimization scheduling model is input with the collected output power of photovoltaic arrays and wind turbines, real-time grid electricity price, state of charge of energy storage batteries, connection status of each charging pile, and operating parameters of solid oxide fuel cell (SOFC) stacks. It also sets the health status constraints of solid oxide fuel cells (SOFC), the charge and discharge depth constraints of energy storage batteries, the power exchange limit constraints of the grid, and the power demand constraints of each charging pile.

[0032] Furthermore, the energy management unit uses the collected photovoltaic array output power, wind turbine output power, real-time grid electricity price, energy storage battery state of charge, connection status of each charging pile, and solid oxide fuel cell stack operating parameters as input parameters for a multi-objective optimization scheduling model. The model sets health state constraints for the solid oxide fuel cell stack, limiting its operation to within the allowable power range; sets charge / discharge depth constraints for the energy storage battery, limiting its charge / discharge depth to a safe range; sets power exchange limit constraints for the grid, limiting power exchange with the grid to not exceed the grid's allowable limits; and sets power demand constraints for each charging pile, ensuring that the power demands of each charging pile are met.

[0033] Under the conditions of satisfying the health state constraints of solid oxide fuel cells (SOFC), the charge and discharge depth constraints of energy storage batteries, the power exchange limit constraints of the power grid, and the power demand constraints of each charging pile, a multi-objective optimization scheduling model with the goal of minimizing the total system operating cost is solved to obtain the baseline power setpoint of solid oxide fuel cells (SOFC), the charge and discharge power plan of energy storage batteries, the power exchange plan with the power grid, and the baseline instructions for the power allocation plan of each charging pile in the next 15-minute period.

[0034] Furthermore, the energy management unit employs an optimization algorithm to solve a multi-objective optimization scheduling model aimed at minimizing the total system operating cost, under the conditions of satisfying the health state constraints of the solid oxide fuel cell stack, the charge and discharge depth constraints of the energy storage battery, the power exchange limit constraints of the power grid, and the power demand constraints of each charging pile. This yields the baseline power setpoint of the solid oxide fuel cell stack, the charge and discharge power plan of the energy storage battery, the power exchange plan with the power grid, and the baseline instructions for the power allocation plan of each charging pile within the next 15-minute period, thus completing the solution of the optimization scheduling model.

[0035] Before the baseline command is executed, the mixing module measures the electrochemical impedance spectrum online through the impedance analysis module, analyzes the impedance spectrum characteristics to calculate the health state coefficient, and dynamically decides the optimal fuel mixing ratio.

[0036] Before the execution of the baseline instructions for the reference power setting of the solid oxide fuel cell (SOFC), the charging and discharging power plan of the energy storage battery, the power exchange plan with the grid, and the power allocation plan of each charging pile in the next 15-minute cycle, the impedance analysis module injects a small signal disturbance of a specific frequency into the solid oxide fuel cell (SOFC) stack.

[0037] Furthermore, before the execution of the baseline instructions for the solid oxide fuel cell stack's baseline power setting, the energy storage battery's charge and discharge power plan, the power exchange plan with the grid, and the power allocation plan for each charging pile within the next 15-minute period obtained by the energy management unit, the impedance analysis module injects a set of sinusoidal small-signal current disturbances containing multiple specific frequencies into the solid oxide fuel cell stack via a signal generator. These disturbances may be distributed across multiple frequency points from high frequency to low frequency.

[0038] The impedance analysis module measures the electrochemical impedance spectrum of a solid oxide fuel cell (SOFC) stack online by injecting a small signal perturbation of a specific frequency into the SOFC stack.

[0039] Furthermore, the impedance analysis module injects a small-signal current disturbance of a specific frequency into the solid oxide fuel cell stack and simultaneously measures the voltage response of the solid oxide fuel cell stack to the small-signal current disturbance at each frequency point. Based on the amplitude ratio and phase difference of the voltage response to the current disturbance, the impedance value of the solid oxide fuel cell stack at each frequency point is calculated, and the electrochemical impedance spectrum of the solid oxide fuel cell stack is plotted.

[0040] The energy management unit analyzes the electrochemical impedance spectral characteristics of the solid oxide fuel cell (SOFC) stack based on the online measurement of the electrochemical impedance spectrum by the impedance analysis module.

[0041] Furthermore, the energy management unit uses an equivalent circuit model to fit the electrochemical impedance spectrum of the solid oxide fuel cell stack, which is measured online by the impedance analysis module. From the electrochemical impedance spectrum of the solid oxide fuel cell stack, the charge transfer resistance, which characterizes the electrochemical reaction kinetics, and the areal ratio resistance, which characterizes the ohmic polarization, are extracted.

[0042] Based on the electrochemical impedance spectral characteristics of the analyzed solid oxide fuel cell (SOFC) stack, the energy management unit calculates the health status coefficient, which reflects the internal electrochemical reaction activity and aging degree of the solid oxide fuel cell (SOFC) stack.

[0043] Furthermore, based on the analyzed electrochemical impedance spectroscopy characteristics of the solid oxide fuel cell stack, the energy management unit calculates the health status coefficient, which reflects the electrochemical reaction activity and aging degree inside the solid oxide fuel cell stack, using the formula SOH = initial charge transfer resistance divided by current charge transfer resistance, multiplied by initial areal resistance divided by current areal resistance, and multiplied by initial rated efficiency divided by current actual efficiency.

[0044] The expression for the health status coefficient is: ; in, The health status coefficient of a solid oxide fuel cell stack. This is the charge transfer resistance of the fuel cell in its initial state. The charge transfer resistance is obtained by fitting an electrochemical impedance spectroscopy spectrum. The areal resistivity is obtained by fitting the electrochemical impedance spectroscopy in the high-frequency region. This is the area resistivity of the fuel cell stack in its initial state. This represents the rated operating efficiency of the fuel cell stack in its initial state. This represents the actual operating efficiency of the fuel cell stack under rated operating conditions.

[0045] The energy management unit acquires the green hydrogen flow rate produced by the water electrolysis hydrogen production unit, the reference power setting value of the solid oxide fuel cell (SOFC) in the next 15-minute cycle, and the health status coefficient reflecting the electrochemical reaction activity and aging degree inside the solid oxide fuel cell (SOFC) stack.

[0046] Furthermore, the energy management unit obtains the green hydrogen flow rate produced by the water electrolysis hydrogen production unit from the flow meter of the water electrolysis hydrogen production unit, obtains the reference power setting value of the solid oxide fuel cell stack for the next 15-minute period from the reference instructions stored in the energy management unit, and obtains the health status coefficient reflecting the internal electrochemical reaction activity and aging degree of the solid oxide fuel cell stack from the health status calculation module.

[0047] The green hydrogen flow rate produced by the water electrolysis hydrogen production unit, the baseline power setting of the solid oxide fuel cell (SOFC) in the next 15-minute cycle, and the health status coefficient reflecting the electrochemical reaction activity and aging degree inside the solid oxide fuel cell (SOFC) stack are input into the fuel mixing decision function.

[0048] Furthermore, the energy management unit inputs the green hydrogen flow rate produced by the water electrolysis hydrogen production unit, the baseline power setting value of the solid oxide fuel cell stack in the next 15-minute cycle, and the health status coefficient reflecting the internal electrochemical reaction activity and aging degree of the solid oxide fuel cell stack into a preset fuel mixing decision function.

[0049] By using the fuel mixing decision function, the dynamic optimal mixing ratio of natural gas and green hydrogen in solid oxide fuel cell (SOFC) fuel is obtained.

[0050] Furthermore, the energy management unit uses a fuel mixing decision function to determine the dynamic optimal mixing ratio of natural gas and green hydrogen in the solid oxide fuel cell fuel, based on the green hydrogen flow rate produced by the water electrolysis hydrogen production unit, the baseline power setting of the solid oxide fuel cell stack in the next 15-minute period, and the health status coefficient reflecting the electrochemical reaction activity and aging degree inside the solid oxide fuel cell stack. For example, it increases the green hydrogen ratio when the health status coefficient is high and decreases the green hydrogen ratio when the health status coefficient is low.

[0051] The execution module, based on the optimized and adjusted instructions, sends control commands to each unit for execution.

[0052] Based on the dynamic optimal mixing ratio of natural gas and green hydrogen in the solid oxide fuel cell (SOFC) fuel, the energy management unit optimizes and adjusts the baseline power setpoint of the SOFC, the charging and discharging power plan of the energy storage battery, the power exchange plan with the grid, and the power allocation plan of each charging station within the next 15-minute period, which are obtained by the energy management unit. This generates optimized instructions that include the target power and fuel command of the SOFC, the charging and discharging power command of the energy storage battery, the power exchange command with the grid, and the power allocation command of each charging station.

[0053] Furthermore, based on the dynamic optimal mixing ratio of natural gas and green hydrogen in the solid oxide fuel cell fuel, the energy management unit optimizes and adjusts the baseline power setpoint of the solid oxide fuel cell stack, the charging and discharging power plan of the energy storage battery, the power exchange plan with the grid, and the power allocation plan of each charging pile within the next 15-minute period, as solved by the energy management unit. This generates optimized instructions that include the target power and fuel command of the solid oxide fuel cell, the charging and discharging power command of the energy storage battery, the power exchange command with the grid, and the power allocation command of each charging pile.

[0054] The optimized target power and fuel command for the solid oxide fuel cell (SOFC) in the optimized and adjusted instructions are sent to the local controller of the solid oxide fuel cell (SOFC).

[0055] Furthermore, the energy management unit sends the optimized target power and fuel command for the solid oxide fuel cell from the optimized instructions to the local controller of the solid oxide fuel cell via the communication bus.

[0056] The optimized charging and discharging power command of the energy storage battery in the optimized and adjusted command is sent to the energy storage converter, and the optimized power exchange command with the grid in the optimized and adjusted command is sent to the grid-connected inverter.

[0057] Furthermore, the energy management unit sends the optimized charging and discharging power command of the energy storage battery from the optimized instructions to the energy storage converter, and sends the optimized power exchange command with the grid from the optimized instructions to the grid-connected inverter.

[0058] The optimized power allocation instructions for each charging pile are sent to each charging pile controller. The local controller of the solid oxide fuel cell (SOFC), the energy storage converter, the grid-connected inverter, and each charging pile controller receive and execute the optimized instructions.

[0059] Furthermore, the energy management unit sends the optimized power allocation instructions for each charging pile from the optimized instructions to each charging pile controller. The solid oxide fuel cell local controller, energy storage converter, grid-connected inverter, and each charging pile controller receive and execute the optimized instructions through their respective communication interfaces.

[0060] The control module and energy management unit dynamically aggregate electric vehicles that support V2G functionality into an equivalent virtual energy storage stack, compare power differences in real time, and prioritize the use of the virtual energy stack for second-level power balance control.

[0061] The energy management unit obtains the connection status of each currently connected charging station, identifies electric vehicles that support V2G functionality from the connection status of each charging station, and obtains the remaining battery capacity, charging and discharging power limits, and the minimum charge level set by the owner for leaving the site from the identified electric vehicles that support V2G functionality.

[0062] Furthermore, the energy management unit polls the controllers of each currently connected charging pile through the communication interface to obtain the connection status information of each charging pile. From the obtained connection status information of each charging pile, it identifies the charging piles that report supporting the V2G function protocol, and then identifies the electric vehicles that support the V2G function connected to these charging piles. Subsequently, the energy management unit initiates a query request to the on-board BMS of the identified electric vehicles that support the V2G function to obtain the remaining battery capacity, maximum charging and discharging power limit, and the minimum battery level preset by the owner through the human-machine interface for leaving the site.

[0063] Based on the acquired remaining battery capacity, charging and discharging power limits, and the minimum charge level set by the vehicle owner for leaving the site, the energy management unit dynamically aggregates all currently connected electric vehicles that support V2G functionality into an equivalent virtual energy storage stack that can be quickly charged and discharged.

[0064] Furthermore, based on the remaining battery capacity, maximum charge / discharge power limit, and the owner's preset minimum charge level for leaving the site of each electric vehicle supporting V2G, the energy management unit calculates the current schedulable discharge capacity and schedulable charging capacity of each electric vehicle. Then, it arithmetically sums the schedulable discharge capacity and schedulable charging capacity of all electric vehicles supporting V2G, and arithmetically sums the maximum discharge power limit and maximum charging power limit of all electric vehicles supporting V2G. This dynamically aggregates all currently connected electric vehicles supporting V2G into a virtual energy storage stack with an equivalent total schedulable capacity and an equivalent total charge / discharge power limit that can be quickly charged and discharged.

[0065] During the execution of optimized instructions by the local controller of the solid oxide fuel cell (SOFC), the energy storage converter, the grid-connected inverter, and the controllers of each charging pile, the actual output of the solid oxide fuel cell (SOFC), the actual output of the photovoltaic array and the wind turbine, and the total charging demand including electric vehicles that support V2G and electric vehicles that do not support V2G are collected.

[0066] Furthermore, during the execution of optimized and adjusted instructions by the solid oxide fuel cell local controller, energy storage converter, grid-connected inverter, and each charging pile controller, the energy management unit reads the actual output of the solid oxide fuel cell in real time from the solid oxide fuel cell local controller, the actual output of the photovoltaic array in real time from the photovoltaic inverter, the actual output of the wind turbine in real time from the wind power converter, and the total charging power demand of all charging piles in real time from each charging pile controller. This total charging power demand includes the charging power demand of electric vehicles that support V2G and electric vehicles that do not support V2G.

[0067] Based on the real-time collected actual output of solid oxide fuel cells (SOFC), the actual output of photovoltaic arrays and wind turbines, and the total charging demand of electric vehicles including those with and without V2G functionality, the power difference between the sum of the actual output of solid oxide fuel cells (SOFC) and the actual output of photovoltaic arrays and wind turbines and the total charging demand of electric vehicles including and without V2G functionality is calculated and compared.

[0068] Furthermore, based on the real-time collected data on the actual output of the solid oxide fuel cell, the actual output of the photovoltaic array, the actual output of the wind turbine, and the total charging demand of electric vehicles including those supporting V2G and those not supporting V2G, the energy management unit calculates the real-time power difference in each sampling cycle, for example, per second, by adding the actual output of the solid oxide fuel cell to the actual output of the photovoltaic array and the actual output of the wind turbine, and then subtracting the total charging demand of electric vehicles including those supporting V2G and those not supporting V2G.

[0069] The expression for real-time power difference is:

[0070] in, For at any time The real-time power difference, For solid oxide fuel cells at time Actual output For photovoltaic array at time Actual output For wind turbines at all times Actual output For at any time This includes the total charging power demand for electric vehicles that support V2G and those that do not. For a specific moment; When the calculated power difference exceeds a set threshold (e.g., 1kW), the equivalent virtual energy storage stack that can be quickly charged and discharged is invoked first through dynamic aggregation. Within a few hundred milliseconds, a discharge or charge command is sent to the equivalent virtual energy storage stack that can be quickly charged and discharged, thereby achieving second-level power balance control.

[0071] Furthermore, when the absolute value of the calculated real-time power difference exceeds the preset power difference threshold, the energy management unit prioritizes calling the dynamically aggregated equivalent virtual energy storage stack that can be quickly charged and discharged. Within a few hundred milliseconds, based on the positive or negative sign of the real-time power difference, the unit issues corresponding discharge or charging commands to the dispatchable electric vehicles supporting V2G function in the virtual energy storage stack through a fast communication protocol to compensate for the power difference and achieve second-level power balance control.

[0072] The update module feeds back the actual operating data to the energy management unit in real time and performs rolling updates to the multi-objective optimization scheduling model.

[0073] Each execution unit generates actual operating data during the execution of instructions, including the actual output of the solid oxide fuel cell (SOFC), the actual charging and discharging power of the energy storage battery, the actual exchange power with the grid, the actual charging power of each charging pile, and the actual response of the dynamically aggregated equivalent virtual energy storage stack that can be charged and discharged quickly.

[0074] Furthermore, when executing the optimized target power and fuel command for the solid oxide fuel cell, the local controller of the solid oxide fuel cell generates the actual output data of the solid oxide fuel cell; when executing the optimized charge and discharge power command for the energy storage battery, the energy storage converter generates the actual charge and discharge power data of the energy storage battery; when executing the optimized power exchange command with the grid, the grid-connected inverter generates the actual power exchange data with the grid; when executing the optimized power allocation command for each charging pile, each charging pile controller generates the actual charging power data of each charging pile; and when executing the second-level power balance control command, the dynamically aggregated equivalent virtual energy storage stack generates the actual response data of the virtual energy storage stack. These data together constitute the actual operating data.

[0075] Each execution unit feeds back the actual operating data to the energy management unit in real time, receives the actual operating data, and obtains the latest output forecasts for ultra-short-term photovoltaic arrays and wind turbines, the latest ultra-short-term charging load forecasts, and the latest real-time electricity price data.

[0076] Furthermore, the local controller for the solid oxide fuel cell transmits the actual output data of the solid oxide fuel cell, the energy storage converter transmits the actual charging and discharging power data of the energy storage battery, the grid-connected inverter transmits the actual power exchange data with the grid, the controllers of each charging pile transmit the actual charging power data of each charging pile, and the control unit of the dynamically aggregated equivalent fast-charging and discharging virtual energy storage stack transmits the actual response data of the virtual energy storage stack. These data are fed back to the energy management unit in real time through the communication network. The energy management unit receives this actual operating data and simultaneously obtains the latest ultra-short-term photovoltaic array output forecast data from the weather forecast server, the latest ultra-short-term wind turbine output forecast data from the weather forecast server, the latest ultra-short-term charging load forecast data from the charging load forecasting algorithm, and the latest real-time electricity price data from the electricity market trading platform.

[0077] Based on actual operating data, combined with the latest output forecasts for ultra-short-term photovoltaic arrays and wind turbines, the latest ultra-short-term charging load forecasts, and the latest real-time electricity price data, the multi-objective optimization scheduling model is continuously updated to generate the baseline instructions for the next calculation cycle.

[0078] Furthermore, based on the received actual operating data, including the actual output of the solid oxide fuel cell, the actual charging and discharging power of the energy storage battery, the actual power exchange with the grid, the actual charging power of each charging pile, and the actual response of the dynamically aggregated equivalent virtual energy storage stack capable of rapid charging and discharging, the energy management unit combines the latest ultra-short-term photovoltaic array output forecast data obtained from the weather forecast server, the latest ultra-short-term wind turbine output forecast data obtained from the weather forecast server, the latest ultra-short-term charging load forecast data obtained from the charging load forecast algorithm, and the latest real-time electricity price data obtained from the electricity market trading platform. It then performs rolling updates to the previously established multi-objective optimization scheduling model. That is, starting from the current moment, based on the latest forecast data and actual operating feedback, it re-executes the model optimization solution to generate the baseline instructions for the next fifteen-minute calculation cycle, including the baseline power setpoint of the solid oxide fuel cell stack, the charging and discharging power plan of the energy storage battery, the power exchange plan with the grid, and the power allocation plan for each charging pile within the next fifteen-minute cycle.

[0079] When the transmission module is shut down, the energy management unit gradually reduces the SOFC power and simultaneously increases energy storage or switches to grid power. After the shutdown, the operating data is uploaded to the cloud platform.

[0080] The energy management unit receives a shutdown command and issues a command to gradually reduce the actual output of the solid oxide fuel cell (SOFC), while simultaneously issuing a command to increase the discharge power of the energy storage battery or increase the power purchased from the grid.

[0081] Furthermore, upon receiving a shutdown command from the operator or the upper-level monitoring platform, the energy management unit immediately issues a command to the local controller of the solid oxide fuel cell, requiring the local controller to gradually reduce the actual output of the solid oxide fuel cell according to a preset power reduction rate. At the same time, the energy management unit issues commands to the energy storage converter to increase the discharge power of the energy storage battery, or to the grid-connected inverter to increase the power purchased from the grid, thereby compensating for the power deficit caused by the reduction in the output of the solid oxide fuel cell and maintaining the continuity of power supply for the total charging needs of electric vehicles that support V2G and those that do not.

[0082] After the actual output of the solid oxide fuel cell (SOFC) drops to the minimum set value, the energy management unit controls the remaining fuel and waste heat to complete the cooling cycle of the solid oxide fuel cell (SOFC) stack.

[0083] Furthermore, when the actual output of the solid oxide fuel cell gradually decreases to a preset minimum power setting value under the control of the solid oxide fuel cell local controller, for example, to 10% of the rated output, the energy management unit gradually reduces and eventually cuts off the fuel supply to the natural gas reformer by controlling the fuel supply valve. At the same time, it controls the thermal management system to use the remaining fuel and high-temperature waste heat of the solid oxide fuel cell stack to perform programmed cooling of the natural gas reformer and the solid oxide fuel cell stack according to the preset cooling curve, thus completing the cooling cycle of the solid oxide fuel cell stack from high-temperature operating state to safe shutdown temperature.

[0084] The energy management unit uploads the actual output of the solid oxide fuel cell (SOFC), the actual output of the photovoltaic array and wind turbine, the actual charging and discharging power of the energy storage battery, the actual power exchanged with the grid, the actual charging power of each charging pile, the actual response of the dynamically aggregated equivalent virtual energy storage stack that can be charged and discharged quickly, and the operating performance data of the solid oxide fuel cell (SOFC) stack to the cloud platform.

[0085] Furthermore, the energy management unit records and stores historical data throughout the entire operating cycle in a local database, including the actual output data sequence of the solid oxide fuel cell, the actual output data sequence of the photovoltaic array, the actual output data sequence of the wind turbine, the actual charge and discharge power data sequence of the energy storage battery, the actual power exchange data sequence with the grid, the actual charging power data sequence of each charging pile, the actual response data sequence of the dynamically aggregated equivalent virtual energy storage stack that can be quickly charged and discharged, and the operating performance data of the solid oxide fuel cell stack, such as temperature, voltage, current, efficiency, and health status coefficient at various time points. This data is packaged and uploaded to a designated cloud platform server for centralized storage and subsequent analysis via a secure network communication protocol.

[0086] The optimization module, upon restart, the energy management unit executes a self-test procedure and formulates the optimal startup strategy, and after startup is complete, re-enters the data acquisition phase.

[0087] Based on the operating performance data of solid oxide fuel cell (SOFC) stacks uploaded to the cloud platform, the optimal start-up temperature rise curve of the solid oxide fuel cell (SOFC) stack is analyzed and predicted through a digital twin model of SOFC performance degradation.

[0088] Furthermore, the energy management unit downloads the operating performance data of the solid oxide fuel cell stack uploaded before shutdown from the cloud platform server, including historical operating temperature, voltage, current, efficiency, health status coefficient, and other data sequences. This data is then input into a pre-established digital twin model of solid oxide fuel cell performance degradation. This model is built based on the material properties, operating history, and aging mechanism of the stack. Through model simulation analysis, the thermal stress distribution and electrochemical reaction kinetics of the solid oxide fuel cell stack under the current health state are analyzed. The model predicts the optimal start-up temperature rise curve under multiple constraints such as start-up time, thermal shock limits, and stack life impact, which is the optimal trajectory of temperature change over time from ambient temperature to the target operating temperature.

[0089] Based on the optimal start-up temperature rise curve of the solid oxide fuel cell (SOFC) stack predicted by the digital twin model of performance degradation, the optimal start-up strategy of the solid oxide fuel cell (SOFC) stack is formulated.

[0090] Furthermore, the energy management unit predicts the optimal start-up temperature rise curve of the solid oxide fuel cell stack based on the digital twin model of solid oxide fuel cell performance degradation. Combined with real-time information such as the current state of charge of the energy storage battery, grid electricity price, and charging load demand, it formulates the optimal start-up strategy for the solid oxide fuel cell stack. This strategy includes the ignition sequence of the start-up burner, fuel supply flow control curve, air supply flow control curve, temperature rise rate control curve, stack voltage and current monitoring thresholds, and abnormal protection logic during the start-up process. This ensures that the solid oxide fuel cell stack safely and stably heats up to the target operating temperature in the shortest possible time, while minimizing start-up energy consumption and the impact on stack life.

[0091] Based on the optimal start-up strategy of the solid oxide fuel cell (SOFC) stack, control commands are sent to the start-up burner or external electric heater to initiate programmed heating of the natural gas reformer and the solid oxide fuel cell (SOFC) stack.

[0092] Furthermore, based on the optimal start-up strategy of the solid oxide fuel cell stack, the energy management unit sends control commands to the starter burner controller or external electric heater controller via the communication bus. This controls the starter burner to ignite according to the preset ignition sequence and fuel supply flow curve, or controls the external electric heater to heat according to the preset power output curve, thus initiating the programmed temperature rise of the natural gas reformer and the solid oxide fuel cell stack. Simultaneously, the energy management unit monitors the temperature, pressure, flow rate, and other parameters of the natural gas reformer and the solid oxide fuel cell stack in real time, and dynamically adjusts the power output of the starter burner or external electric heater based on the monitoring data to ensure that the temperature rise process strictly follows the optimal start-up temperature rise curve.

[0093] After the solid oxide fuel cell (SOFC) stack completes programmed temperature rise and start-up, it re-enters the stage of collecting data on the output power of the photovoltaic array and wind turbine, real-time grid electricity price, state of charge of the energy storage battery, connection status of each charging pile, and operating parameters of the solid oxide fuel cell (SOFC) stack.

[0094] Furthermore, after the solid oxide fuel cell stack completes its programmed temperature rise and start-up, reaches the target operating temperature, and the voltage and current stabilize within the rated range, the energy management unit confirms that the solid oxide fuel cell stack has entered normal operation. Subsequently, it re-enters the data acquisition phase, which involves acquiring the output power of the photovoltaic array from the photovoltaic inverter, the output power of the wind turbine from the wind power converter, the real-time electricity price from the grid dispatch system, the state of charge of the energy storage battery from the energy storage battery management system, the connection status of each charging pile from the controller of each charging pile, and the operating parameters of the solid oxide fuel cell stack from the local controller of the solid oxide fuel cell stack. This provides real-time data input for the next round of multi-objective optimization scheduling model solution and operation control.

[0095] In summary, this invention achieves the following: A data acquisition module collects multi-source data and performs self-checks; a multi-objective optimization scheduling model is used to solve for the baseline command; before command execution, a mixing module measures the electrochemical impedance spectroscopy online through impedance analysis and calculates the health state coefficient, dynamically determining the optimal fuel mixing ratio; an execution module controls the operation of each unit based on the optimized command; a control module dynamically aggregates V2G electric vehicles into a virtual energy storage stack, achieving second-level power balance; an update module continuously updates the optimization model through real-time feedback data; a transmission module performs orderly shutdown and uploads data during shutdown; and an optimization module formulates the optimal startup strategy based on a digital twin model during restart, completing the system restart.

[0096] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A smart charging system for natural gas SOFCs with multi-energy complementarity, characterized in that: It includes a data acquisition module, an energy management unit that executes a self-test procedure and collects multi-source data, and based on the collected real-time data, the energy management unit uses a multi-objective optimization scheduling model to solve for the baseline command; Before the baseline command is executed, the mixing module measures the electrochemical impedance spectrum online through the impedance analysis module, analyzes the impedance spectrum characteristics to calculate the health state coefficient, and dynamically decides the optimal fuel mixing ratio. The execution module, based on the optimized and adjusted instructions, sends control instructions to each unit for execution by the energy management unit; The control module and energy management unit dynamically aggregate electric vehicles that support V2G functionality into an equivalent virtual energy storage stack, compare power differences in real time, and prioritize the use of the virtual energy stack for second-level power balance control. The update module feeds back the actual operating data to the energy management unit in real time and performs rolling updates to the multi-objective optimization scheduling model. When the transmission module is shut down, the energy management unit gradually reduces the SOFC power and simultaneously increases energy storage or switches to grid power. After the shutdown, the operating data is uploaded to the cloud platform. The optimization module, upon restart, the energy management unit executes a self-test procedure and formulates the optimal startup strategy, and after startup is complete, re-enters the data acquisition phase.

2. The intelligent charging system for natural gas SOFCs oriented towards multi-energy complementarity as described in claim 1, characterized in that: The energy management unit performs a self-test procedure and collects multi-source data, including the following steps: The energy management unit collects the output power of the photovoltaic array and wind turbine, the real-time electricity price of the grid, the state of charge of the energy storage battery, the connection status of each charging pile, and the operating parameters of the solid oxide fuel cell (SOFC) stack. The energy management unit initiates self-test procedures for the valves, flow meters, and pressure sensors of the natural gas supply unit; the voltage, temperature, and sealing of the solid oxide fuel cell (SOFC) stack; the circuit breaker and converter status of the power electronic equipment; and the photovoltaic inverter, wind power converter, grid connection point, and energy storage battery BMS of the multi-energy interface. The self-test procedure of the energy management unit confirms the status of valves, flow meters, and pressure sensors in the natural gas supply unit, the voltage, temperature, and sealing of the solid oxide fuel cell (SOFC) stack, the status of circuit breakers and converters in the power electronic equipment, and that the photovoltaic inverters, wind power converters, grid connection points, and energy storage battery BMS of the multi-energy interface are in normal working condition.

3. The intelligent charging system for natural gas SOFCs oriented towards multi-energy complementarity as described in claim 2, characterized in that: Based on the collected real-time data, the energy management unit uses a multi-objective optimization scheduling model to obtain the baseline command, including the following steps: Based on the collected output power of photovoltaic arrays and wind turbines, real-time grid electricity prices, state of charge of energy storage batteries, connection status of each charging pile, and operating parameters of solid oxide fuel cell (SOFC) stacks, a multi-objective optimization scheduling model is established with the goal of minimizing the total system operating cost. The multi-objective optimization scheduling model includes natural gas cost, electricity purchase cost, electricity sales revenue, and carbon trading revenue. The multi-objective optimization scheduling model is input with the collected output power of photovoltaic arrays and wind turbines, real-time grid electricity price, state of charge of energy storage batteries, connection status of each charging pile, and operating parameters of solid oxide fuel cell (SOFC) stacks. It also sets the health status constraints of solid oxide fuel cells (SOFC), the charge and discharge depth constraints of energy storage batteries, the power exchange limit constraints of the grid, and the power demand constraints of each charging pile. Under the conditions of satisfying the health state constraints of solid oxide fuel cells (SOFC), the charge and discharge depth constraints of energy storage batteries, the power exchange limit constraints of the power grid, and the power demand constraints of each charging pile, a multi-objective optimization scheduling model with the goal of minimizing the total system operating cost is solved to obtain the baseline power setpoint of solid oxide fuel cells (SOFC), the charge and discharge power plan of energy storage batteries, the power exchange plan with the power grid, and the baseline instructions for the power allocation plan of each charging pile in the next 15-minute period.

4. The intelligent charging system for natural gas SOFCs oriented towards multi-energy complementarity as described in claim 3, characterized in that: Before the baseline command is executed, the electrochemical impedance spectroscopy is measured online using the impedance analysis module. The health state coefficient is obtained by analyzing the characteristics of the impedance spectrum, including the following steps: Before the execution of the baseline instructions for the reference power setting of the solid oxide fuel cell (SOFC), the charging and discharging power plan of the energy storage battery, the power exchange plan with the grid, and the power allocation plan of each charging pile in the next 15-minute period, the impedance analysis module injects a small signal disturbance of a specific frequency into the solid oxide fuel cell (SOFC) stack. The impedance analysis module measures the electrochemical impedance spectrum of a solid oxide fuel cell (SOFC) stack online by injecting a small signal perturbation of a specific frequency into the SOFC stack. The energy management unit analyzes the electrochemical impedance spectroscopy characteristics of the solid oxide fuel cell (SOFC) stack based on the online measurement of the electrochemical impedance spectroscopy by the impedance analysis module. Based on the electrochemical impedance spectral characteristics of the analyzed solid oxide fuel cell (SOFC) stack, the energy management unit calculates the health status coefficient, which reflects the internal electrochemical reaction activity and aging degree of the solid oxide fuel cell (SOFC) stack.

5. The intelligent charging system for natural gas SOFCs oriented towards multi-energy complementarity as described in claim 4, characterized in that: Dynamically determining the optimal fuel mix ratio includes the following steps: The energy management unit acquires the green hydrogen flow rate produced by the water electrolysis hydrogen production unit, the reference power setting value of the solid oxide fuel cell (SOFC) in the next 15-minute cycle, and the health status coefficient reflecting the electrochemical reaction activity and aging degree inside the solid oxide fuel cell (SOFC) stack. The green hydrogen flow rate produced by the water electrolysis hydrogen production unit, the baseline power setting value of the solid oxide fuel cell (SOFC) in the next 15-minute cycle, and the health status coefficient reflecting the electrochemical reaction activity and aging degree inside the solid oxide fuel cell (SOFC) stack are input into the fuel mixing decision function. By using the fuel mixing decision function, the dynamic optimal mixing ratio of natural gas and green hydrogen in solid oxide fuel cell (SOFC) fuel is obtained.

6. The intelligent charging system for natural gas SOFCs oriented towards multi-energy complementarity as described in claim 5, characterized in that: Based on the optimized and adjusted instructions, the energy management unit will issue control instructions to each unit for execution, including the following steps: Based on the dynamic optimal mixing ratio of natural gas and green hydrogen in the solid oxide fuel cell (SOFC) fuel, the energy management unit optimizes and adjusts the baseline power setpoint of the SOFC, the charging and discharging power plan of the energy storage battery, the power exchange plan with the grid, and the power allocation plan of each charging pile within the next 15-minute period obtained by the energy management unit. This generates optimized instructions that include the target power and fuel command of the SOFC, the charging and discharging power command of the energy storage battery, the power exchange command with the grid, and the power allocation command of each charging pile. The optimized target power and fuel command for the solid oxide fuel cell (SOFC) in the optimized and adjusted instructions are sent to the local controller of the solid oxide fuel cell (SOFC); The optimized charging and discharging power command of the energy storage battery in the optimized and adjusted command is sent to the energy storage converter, and the optimized power exchange command with the grid in the optimized and adjusted command is sent to the grid-connected inverter. The optimized power allocation instructions for each charging pile are sent to each charging pile controller. The local controller of the solid oxide fuel cell (SOFC), the energy storage converter, the grid-connected inverter, and each charging pile controller receive and execute the optimized instructions.

7. The intelligent charging system for natural gas SOFCs oriented towards multi-energy complementarity as described in claim 6, characterized in that: The energy management unit dynamically aggregates V2G-enabled electric vehicles into an equivalent virtual energy storage stack, compares power differences in real time, and prioritizes the use of the virtual stack for second-level power balance control, including the following steps: The energy management unit obtains the connection status of each currently connected charging pile, identifies electric vehicles that support V2G functionality from the connection status of each charging pile, and obtains the remaining battery capacity, charging and discharging power limits, and the minimum charge level set by the owner for leaving the site from the identified electric vehicles that support V2G functionality. Based on the acquired remaining battery capacity, charging and discharging power limits, and the minimum charge level set by the vehicle owner for leaving the site, the energy management unit dynamically aggregates all currently connected electric vehicles that support V2G functionality into an equivalent virtual energy storage stack that can be quickly charged and discharged. During the execution of optimized and adjusted instructions by the local controller of solid oxide fuel cell (SOFC), energy storage converter, grid-connected inverter, and each charging pile controller, the actual output of solid oxide fuel cell (SOFC), the actual output of photovoltaic array and wind turbine, and the total charging demand including electric vehicles with V2G function and electric vehicles without V2G function are collected. Based on the real-time collected actual output of solid oxide fuel cells (SOFC), the actual output of photovoltaic arrays and wind turbines, and the total charging demand including electric vehicles with and without V2G functionality, the power difference between the actual output of solid oxide fuel cells (SOFC) and the sum of the actual output of photovoltaic arrays and wind turbines and the total charging demand including electric vehicles with and without V2G functionality is calculated and compared. When the calculated power difference exceeds a set threshold (e.g., 1kW), the equivalent virtual energy storage stack that can be quickly charged and discharged is invoked first through dynamic aggregation. Within a few hundred milliseconds, a discharge or charge command is sent to the equivalent virtual energy storage stack that can be quickly charged and discharged, thereby achieving second-level power balance control.

8. The intelligent charging system for natural gas SOFCs oriented towards multi-energy complementarity as described in claim 7, characterized in that: The actual operational data is fed back to the energy management unit in real time, and the multi-objective optimization scheduling model is updated on a rolling basis, including the following steps: Each execution unit generates actual operating data during the execution of instructions, including the actual output of solid oxide fuel cells (SOFC), the actual charging and discharging power of energy storage batteries, the actual exchange power with the grid, the actual charging power of each charging pile, and the actual response of the dynamically aggregated equivalent virtual energy storage stack that can be charged and discharged quickly. Each execution unit feeds back the actual operating data to the energy management unit in real time, receives the actual operating data, and obtains the latest output forecasts of ultra-short-term photovoltaic arrays and wind turbines, the latest ultra-short-term charging load forecasts, and the latest real-time electricity price data. Based on actual operating data, combined with the latest output forecasts for ultra-short-term photovoltaic arrays and wind turbines, the latest ultra-short-term charging load forecasts, and the latest real-time electricity price data, the multi-objective optimization scheduling model is continuously updated to generate the baseline instructions for the next calculation cycle.

9. The intelligent charging system for natural gas SOFCs oriented towards multi-energy complementarity as described in claim 8, characterized in that: When the system shuts down, the energy management unit gradually reduces the SOFC power and simultaneously increases energy storage or switches to grid power. After the shutdown, the operating data is uploaded to the cloud platform, including the following steps: The energy management unit receives a shutdown command and issues a command to gradually reduce the actual output of the solid oxide fuel cell (SOFC), while simultaneously issuing a command to increase the discharge power of the energy storage battery or increase the power purchased from the grid. After the actual output of a solid oxide fuel cell (SOFC) drops to the minimum set value, the energy management unit controls the remaining fuel and waste heat to complete the cooling cycle of the SOFC stack. The energy management unit uploads the actual output of the solid oxide fuel cell (SOFC), the actual output of the photovoltaic array and wind turbine, the actual charging and discharging power of the energy storage battery, the actual power exchanged with the grid, the actual charging power of each charging pile, the actual response of the dynamically aggregated equivalent virtual energy storage stack that can be charged and discharged quickly, and the operating performance data of the solid oxide fuel cell (SOFC) stack to the cloud platform.

10. The intelligent charging system for natural gas SOFCs oriented towards multi-energy complementarity as described in claim 9, characterized in that: Upon restart, the energy management unit executes a self-test procedure and determines the optimal startup strategy. After startup, it re-enters the data acquisition phase, including the following steps: Based on the operating performance data of solid oxide fuel cell (SOFC) stacks uploaded to the cloud platform, the optimal start-up temperature rise curve of solid oxide fuel cell (SOFC) stacks is analyzed and predicted through a digital twin model of solid oxide fuel cell (SOFC) performance degradation. Based on the optimal start-up temperature rise curve of the solid oxide fuel cell (SOFC) stack predicted by the digital twin model of solid oxide fuel cell (SOFC) performance degradation, the optimal start-up strategy of the solid oxide fuel cell (SOFC) stack is formulated. Based on the optimal start-up strategy of the solid oxide fuel cell (SOFC) stack, control commands are sent to the start-up burner or external electric heater to initiate programmed heating of the natural gas reformer and the solid oxide fuel cell (SOFC) stack. After the solid oxide fuel cell (SOFC) stack completes programmed temperature rise and start-up, it re-enters the stage of collecting data on the output power of the photovoltaic array and wind turbine, real-time grid electricity price, state of charge of the energy storage battery, connection status of each charging pile, and operating parameters of the solid oxide fuel cell (SOFC) stack.

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