Scheduling control method and device for SOFC power generation system and electronic equipment

Abstract

The application provides a scheduling control method and device for a SOFC power generation system and electronic equipment, relates to the field of SOFC power generation system scheduling control, and unifies the working state, the thermal standby depth, the start-stop time delay, the fuel consumption curve, the DCDC efficiency curve, the DCAC efficiency curve and the grid-connected peak regulation power instruction of each SOFC power generation unit into a mixed integer optimization model, reduces fuel consumption, improves low-load operation efficiency under the premise of guaranteeing peak regulation power tracking capability and equipment operation constraints, and takes into account system dynamic response flexibility.

Description

Technical Field

[0001] This invention relates to the field of SOFC power generation system dispatch control, and in particular to a dispatch control method, apparatus and electronic equipment for SOFC power generation systems. Background Technology

[0002] Fuel cells can directly convert the chemical energy in fuels such as hydrogen, syngas, natural gas, and ammonia into electrical energy. They are characterized by high energy conversion efficiency, low pollutant emissions, and high modularity, making them an important technological route for hydrogen energy power generation and efficient distributed energy supply.

[0003] Among various types of fuel cells, SOFC (Solid Oxide Fuel Cell) offers advantages such as high efficiency, strong fuel adaptability, the ability to utilize non-precious metal catalysts, and coupling with combined heat and power (CHP) systems. The electrochemical reaction temperature of SOFCs is typically around 600 to 800 degrees Celsius, achieving high power generation efficiency near rated operating conditions. However, to maintain this high-temperature operating environment, SOFC power generation systems require continuous compensation for heat losses and stable operation of auxiliary processes such as fuel distribution, reforming, heat exchange, and exhaust gas combustion. Therefore, when a single SOFC power generation unit operates at low load for an extended period, the fuel consumption per unit output power increases significantly, leading to a substantial decrease in the efficiency of the SOFC power generation system.

[0004] On the other hand, due to limitations in fuel cell stack manufacturing, thermal management, mechanical structure, and reliability, the rated power of a single SOFC generating unit is usually difficult to directly reach the megawatt level. In engineering, it is more common to use multiple SOFC generating units with capacities of hundreds of kilowatts or smaller to form a large-capacity power generation system. The multi-unit structure brings the possibility of independently controlling the power and operating status of each unit: when the total load is low, some units can be put into a thermal standby state, so that they do not output electrical power but only maintain a certain temperature with low fuel consumption; the remaining units operate in a higher efficiency range, thereby improving the overall system efficiency.

[0005] Existing dispatching methods for multiple SOFC power generation systems primarily focus on power sharing, simple economic allocation, or load distribution under a single operating state. They typically fail to systematically consider the multi-state switching mechanisms of high-temperature SOFC equipment, constraints on thermal standby depth variations, start-up and shutdown delays, and the impact of frequent thermal cycling on equipment lifespan. If only an average power distribution strategy is followed, all units will be in low-load regions under off-peak load commands, leading to higher fuel consumption. Conversely, if some units are only partially shut down, the slow start-up process and limited temperature variations may prevent timely response to subsequent load increase commands.

[0006] Furthermore, actual megawatt-level SOFC power generation systems are not solely composed of SOFC stacks. Each SOFC unit typically needs to be connected to the DC bus via a DC-DC converter, and then output to the AC side or grid connection point via a DC-AC inverter. The efficiency of both DC-DC and DC-AC varies with output power. If converter losses are ignored in the scheduling model, there may be a discrepancy between the optimized SOFC output power and the actual available power on the grid side, especially under low load, multi-branch parallel operation, and inverter group operation scenarios. Summary of the Invention

[0007] In view of this, the purpose of the present invention is to provide a scheduling and control method, device and electronic equipment for SOFC power generation systems. The method integrates the working status, hot standby depth, start-stop delay, fuel consumption curve, DC-DC efficiency curve, DC-AC efficiency curve and grid-connected peak power command of each SOFC power generation unit into a mixed integer optimization model. Under the premise of ensuring peak power tracking capability and equipment operation constraints, it reduces fuel consumption, improves low-load operation efficiency, and takes into account the flexibility of system dynamic response.

[0008] In a first aspect, embodiments of the present invention provide a scheduling and control method for an SOFC power generation system, the SOFC power generation system comprising multiple SOFC power generation units, a DC-DC converter correspondingly connected to each SOFC power generation unit, and at least one DC-AC inverter; the method includes: By utilizing the mutual exclusion relationship of the working states of each SOFC power generation unit during the scheduling cycle, a state model corresponding to each SOFC power generation unit is constructed. Using the operating state, output power, fuel consumption, thermal standby depth, DC-DC converter output power, and DC-AC inverter output power of each SOFC power generation unit in the state model as decision variables, a mixed integer optimization model is constructed with the goal of minimizing the total fuel consumption and start-up / shutdown cost of the SOFC power generation system within the scheduling cycle. After constructing the constraints of the mixed integer optimization model based on the target peak-shaving power of the SOFC power generation system, the real-time control parameters corresponding to the decision variables when the mixed integer optimization model satisfies the decision objective under the constraints are obtained. The operating status of each SOFC power generation unit, DC-DC converter, and DC-AC inverter is scheduled in real time by controlling parameters.

[0009] Optionally, the step of constructing a state model for each SOFC power generation unit by utilizing the mutual exclusion relationship of the operating states of each SOFC power generation unit during the scheduling cycle includes: Obtain the operating status of each SOFC power generation unit during the scheduling cycle; the operating status includes at least: normal operation status, hot standby status, shutdown process status, startup process status, and cold state status; The mutual exclusion relationship between the various states in the working state is determined by the operating temperature and output power of the SOFC power generation unit under normal operating conditions, the fuel consumption value and standby temperature of the SOFC power generation unit under hot standby conditions, the duration of the shutdown process state and the startup process state, and the shutdown duration of the SOFC power generation system under cold conditions. A state model for each SOFC power generation unit is constructed based on mutual exclusion relationships; wherein, the state model satisfies the following relationship: ; , , , , The variables are 0-1, representing the state of the i-th SOFC power generation unit in normal operation, hot standby, shutdown process, startup process, and cold state in the t-th time period, respectively; N is the total number of SOFC power generation units; and T is the total duration of the scheduling cycle.

[0010] Optionally, the process of obtaining the hot standby depth includes: The fuel consumption change of each SOFC power generation unit in the hot standby state is calculated by the fuel consumption of each SOFC power generation unit in the hot standby state and the hot standby baseline fuel consumption. The standby depth of each SOFC power generation unit is obtained based on the change in fuel consumption; the standby depth is calculated using the following formula: ; The fuel consumption of the i-th SOFC power generation unit in the hot standby state during the t-th time period; The thermal standby reference fuel consumption of the i-th SOFC power generation unit; For the i-th SOFC power generation unit, the change in fuel consumption is... The coefficient of variation in; Let be the thermal standby depth of the i-th SOFC power generation unit during the t-th time period.

[0011] Optionally, the process of obtaining the output power of the DC-DC converter includes: Based on the quadratic function fitting relationship between each SOFC power generation unit and the corresponding DC-DC converter, the quadratic fitting coefficient, the first-order fitting coefficient, and the constant fitting coefficient of the DC-DC converter are obtained. The input power of the corresponding DC-DC converter is determined based on the output power of each SOFC power generation unit. The output power of the DC-DC converter is calculated using its input power, quadratic fitting coefficient, first-order fitting coefficient, and constant fitting coefficient. The output power is obtained using the following formula: ; Let be the output power of the i-th DC-DC converter in the t-th time period; Let be the input power of the i-th DC-DC converter in the t-th time period; The coefficients are the quadratic fitting coefficients of the i-th DC-DC converter; The first-order fitting coefficients of the i-th DC-DC converter; represents the constant fitting coefficients of the i-th DC-DC converter.

[0012] Optionally, the process of obtaining the output power of the DCAC inverter includes: The efficiency curve of the DCAC inverter is obtained by using historical operating data of the SOFC power generation system, and the conversion efficiency of the DCAC inverter is obtained by using the efficiency curve. The input power of the DCAC inverter is determined based on the power accumulation result of all DC-DC converters. The output power of the DCAC inverter is calculated using the input power and conversion efficiency; the output power of the DCAC inverter is obtained through the following formula: ; Let g be the output power of the g-th DCAC inverter in the t-th time period; Let be the input power of the g-th DCAC inverter in the t-th time period; Let be the conversion efficiency of the g-th DC-AC inverter in the t-th time period.

[0013] Optionally, the objective function corresponding to the decision objective is: ; in, The output value of the objective function; Let be the fuel consumption of the i-th SOFC power generation unit in the t-th time period; Let i represent the shutdown process state of the i-th SOFC power generation unit during the t-th time period. Let be the fuel consumption of the i-th SOFC power generation unit during the shutdown process. The shutdown cost of the i-th SOFC power generation unit in the t-th time period; Let i represent the startup process state of the i-th SOFC power generation unit in the t-th time period. Let be the fuel consumption of the i-th SOFC power generation unit during the startup process. The startup cost of the i-th SOFC power generation unit in the t-th time period.

[0014] Optionally, the constraints for constructing a mixed-integer optimization model based on the target peak-shaving power of the SOFC power generation system include: Grid-connected power balance constraints are constructed by using the target peak-shaving power of the SOFC power generation system and the total output power of the DCAC inverter. The state mutual exclusion constraints of the SOFC power generation unit are constructed by the mutual exclusion relationship of the working state of the state model. The state switching timing constraints of the SOFC power generation unit are constructed using the state switching parameters of the SOFC power generation unit in the state model. SOFC power boundary constraints and power ramping constraints are constructed by using the output power range and power change value of each SOFC power generation unit under normal operating conditions. The thermal standby depth constraint is constructed by the thermal standby depth value and its rate of change of each SOFC power generation unit. Piecewise linearization constraints for SOFC fuel consumption, DC-DC efficiency, and DCAC efficiency are constructed using the piecewise linearization results of the SOFC fuel consumption curve, DC-DC efficiency curve, and DCAC efficiency curve of the SOFC power generation system, respectively. The DCAC grouping bus constraint is constructed by using the input power of the DCAC inverter and the total output power of all the DC-DC converters connected to it.

[0015] Optionally, obtain the real-time control parameters corresponding to the decision variables when the mixed-integer optimization model satisfies the decision objective under constraints, including: Solve the objective decision variables corresponding to the mixed-integer optimization model with the decision objective in mind; Based on the target decision variables, obtain one or more real-time control parameters for each SOFC power generation unit, including the target operating status, target output power, target fuel consumption, target thermal standby depth, target DC-DC converter output power, and target DC-AC inverter output power. If the output power of each SOFC generator unit, DC-DC converter, or DC-AC inverter under the real-time control parameters does not meet the power boundary conditions corresponding to the constraints, the real-time control parameters are updated based on the output power until the output power meets the power boundary conditions.

[0016] In a second aspect, the present invention provides a dispatch control device for an SOFC power generation system, the SOFC power generation system comprising a plurality of SOFC power generation units, a DC-DC converter correspondingly connected to each SOFC power generation unit, and at least one DC-AC inverter; the device comprises: State model construction module: used to construct the state model corresponding to each SOFC power generation unit by utilizing the mutual exclusion relationship of the working states of each SOFC power generation unit during the scheduling cycle. Mixed Integer Optimization Model Construction Module: This module is used to construct a mixed integer optimization model with the operating state, output power, fuel consumption, thermal standby depth, DC-DC output power, and DC-AC output power of each SOFC power generation unit in the state model as decision variables, and with the goal of minimizing the total fuel consumption and start-up / shutdown cost of the SOFC power generation system within the scheduling cycle. Real-time control parameter acquisition module: After constructing the constraints of the mixed integer optimization model based on the target peak-shaving power of the SOFC power generation system, it acquires the real-time control parameters corresponding to the decision variables when the mixed integer optimization model satisfies the decision objective under the constraints. Real-time scheduling and control module: Used to schedule the operating status of each SOFC power generation unit, DC-DC converter and DC-AC inverter in real time through real-time control parameters.

[0017] Thirdly, embodiments of the present invention also provide an electronic device, which includes a processor and a memory, the memory storing computer-executable instructions that can be executed by the processor, the processor executing the computer-executable instructions to implement the steps of the scheduling and control method for SOFC power generation systems provided in the first aspect.

[0018] This invention provides a scheduling and control method, apparatus, and electronic device for an SOFC power generation system. The SOFC power generation system includes multiple SOFC power generation units, DC-DC converters connected to each SOFC power generation unit, and at least one DCAC inverter. In the process of scheduling and controlling the various devices in the SOFC power generation system, the method first utilizes the mutual exclusion relationship of the operating states of each SOFC power generation unit during the scheduling cycle to construct a state model corresponding to each SOFC power generation unit. Then, using the operating state, output power, fuel consumption, thermal standby depth, DC-DC converter output power, and DCAC inverter output power of each SOFC power generation unit in the state model as decision variables, a mixed-integer optimization model is constructed with the goal of minimizing the total fuel consumption and start-up / shutdown cost of the SOFC power generation system during the scheduling cycle. Subsequently, after constructing the constraints of the mixed-integer optimization model based on the target peak-shaving power of the SOFC power generation system, the real-time control parameters corresponding to the decision variables when the mixed-integer optimization model satisfies the decision objective under the constraints are obtained. Finally, the operating states of each SOFC power generation unit, DC-DC converter, and DCAC inverter are scheduled in real-time using the real-time control parameters. This method integrates the operating status, hot standby depth, start-stop delay, fuel consumption curve, DC-DC efficiency curve, DC-AC efficiency curve, and grid-connected peak-shaving power command of each SOFC power generation unit into a unified mixed integer optimization model. Under the premise of ensuring peak-shaving power tracking capability and equipment operation constraints, it reduces fuel consumption, improves low-load operation efficiency, and takes into account the flexibility of system dynamic response.

[0019] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0021] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0022] Figure 1 A flowchart of a scheduling and control method for an SOFC power generation system provided in an embodiment of the present invention; Figure 2 A topology diagram of an SOFC power generation system provided in an embodiment of the present invention; Figure 3 This invention provides a state and switching relationship diagram of a state model in a scheduling and control method for an SOFC power generation system. Figure 4 A flowchart of another scheduling and control method for an SOFC power generation system provided in an embodiment of the present invention; Figure 5 A graph showing the steady-state optimal power allocation scheme of a megawatt-level SOFC power generation system in a scheduling and control method for SOFC power generation systems provided in an embodiment of the present invention. Figure 6 A schematic diagram of the 24-hour optimized scheduling scheme for a megawatt-level system under the condition of consistent power generation units in a scheduling and control method for an SOFC power generation system provided in an embodiment of the present invention; Figure 7 A schematic diagram of the 24-hour optimized scheduling scheme for a megawatt-level system under inconsistent power generation units in a scheduling and control method for an SOFC power generation system provided in an embodiment of the present invention; Figure 8 A schematic diagram of the 15min / 96-point optimized scheduling scheme for a megawatt-level system when hot standby is not allowed in a scheduling and control method for an SOFC power generation system provided in an embodiment of the present invention; Figure 9 A schematic diagram of the curve of the 15min / 96-point optimized scheduling scheme of a megawatt-level system during hot standby in a scheduling and control method for SOFC power generation system provided in an embodiment of the present invention; Figure 10 This is a schematic diagram of the structure of a dispatch control device for an SOFC power generation system provided in an embodiment of the present invention; Figure 11 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0023] icon: 100 - State model construction module; 200 - Mixed-integer optimization model construction module; 300 - Real-time control parameter acquisition module; 400 - Real-time scheduling and control module; 101 - Processor; 102 - Memory; 103 - Bus; 104 - Communication interface. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] To facilitate understanding of this embodiment, a scheduling and control method for an SOFC power generation system disclosed in this embodiment of the invention will first be introduced. The SOFC power generation system includes multiple SOFC power generation units, DC-DC converters connected to each SOFC power generation unit, and at least one DC-AC inverter. Based on this, the method is as follows: Figure 1 As shown, it includes: Step S101: Construct the state model corresponding to each SOFC power generation unit by utilizing the mutual exclusion relationship of the working states of each SOFC power generation unit during the scheduling cycle.

[0026] Within the discrete-time scheduling domain, each SOFC power generation unit is divided into multiple operating states, and a unified state evolution model is constructed based on the mutual exclusion criterion of single state in a single time period. Specifically, the operating states of a single SOFC power generation unit in each scheduling period are divided into five categories: normal operation, hot standby, shutdown process, startup process, and cold state. A corresponding 0-1 binary variable is assigned to each state, and the constraint that the sum of the five state variables in a single time period is always equal to 1 ensures that the same power generation unit can only be in one operating state at any given time. On this basis, state switching trigger variables and start-stop delay parameters are introduced to construct state switching timing constraints: when a power generation unit is triggered to shut down from the operation / hot standby side, it must undergo a shutdown process of a set duration before entering the cold state; when a power generation unit is triggered to start from the cold state, it must undergo a startup process of a set duration before returning to the operation or hot standby state. This matches the thermal inertia characteristics of SOFC high-temperature equipment and avoids instantaneous state jumps that do not conform to physical laws. Meanwhile, a continuous variable of hot standby depth is introduced to characterize the degree of temperature reduction of the power generation unit relative to the normal operating temperature under hot standby conditions. Maximum value constraints and adjacent time period change rate constraints are configured for this variable, and the hot standby depth is limited to 0 under normal operating conditions. This realizes the quantitative correlation between hot standby energy consumption and recovery response speed, and completes the construction of a complete multi-state model.

[0027] Step S102: Using the operating state, output power, fuel consumption, thermal standby depth, DC-DC converter output power and DC-AC inverter output power of each SOFC power generation unit in the state model as decision variables, construct a mixed integer optimization model with the decision objective of minimizing the total fuel consumption and start-up / shutdown cost of the SOFC power generation system within the scheduling cycle.

[0028] Using the operating state variables, output power, fuel consumption, and hot standby depth of each SOFC power generation unit in each scheduling period, as well as the corresponding DC-DC converter output power and DCAC inverter output power, as the core decision variables, a mixed-integer linear programming (MILP) optimization model for the entire scheduling cycle is constructed. The optimization objective of the model is to minimize the sum of total system fuel consumption and start-up / shutdown costs within the scheduling cycle. The fuel consumption term includes two parts: first, the fuel consumption of the SOFC power generation unit under normal operating conditions, which has a convex function relationship with the output power. This is transformed into a multi-segment linear constraint through piecewise linearization and coupled with the operating state variables through Big-M constraints to ensure that no operating state fuel consumption is triggered under non-operating conditions; second, the temperature-maintaining fuel consumption under hot standby conditions, whose value is linearly related to the hot standby depth and decreases as the hot standby depth increases. The start-up / shutdown cost term corresponds to the additional fuel consumption and lifespan depreciation costs during shutdown and startup processes, respectively, and is calculated using the corresponding process state variables as weights.

[0029] Furthermore, considering the power loss characteristics of DC-DC converters and DC-AC inverters, the quadratic relationship between their input and output power is synchronously incorporated into the model, and a piecewise linearization method consistent with the SOFC fuel curve is adopted to transform it into a linear constraint, thereby achieving unified modeling of the power mapping relationship between the SOFC side, the DC bus side, and the grid-connected AC side.

[0030] Step S103: After constructing the constraints of the mixed integer optimization model based on the target peak-shaving power of the SOFC power generation system, obtain the real-time control parameters corresponding to the decision variables when the mixed integer optimization model satisfies the decision objective under the constraints.

[0031] Using the system's target peak-shaving power command as the core boundary, full-dimensional operational constraints are configured for the mixed-integer optimization model. The optimal values ​​of each variable satisfying the optimization objective are then obtained and used as the system's real-time control parameters. The specific constraints include: State operation constraints include state mutual exclusion constraints, state transition timing constraints, start and stop duration constraints, and state boundary constraints at the beginning and end of the scheduling cycle. SOFC power constraints include upper and lower limits of output power under normal operating conditions, power ramp-up rate constraints between adjacent time periods, and constraints that force the output power to be 0 under non-power generation conditions. Hot standby depth constraints include upper and lower limits for hot standby depth, depth change rate constraints between adjacent time periods, and depth zeroing constraints under normal operating conditions. Converter power constraints include input-output power mapping constraints for DC-DC converters, input-output power mapping constraints for DC-AC inverters, and grouping constraints where the sum of the output power of all DC-DC converters within the same inverter group equals the corresponding DC-AC input power. Grid-connected power balance constraint: The sum of the AC output power of all DCAC inverters is limited to equal the target peak-shaving power command for the corresponding time period, ensuring that the dispatching result directly meets the power demand of the grid-connected side.

[0032] After constraint configuration is completed, the model is solved using a commercial mixed-integer programming solver to obtain the target operating status, target output power, target hot standby depth, and corresponding target power values ​​for DC-DC and DC-AC for each SOFC power generation unit during each time period of the entire scheduling cycle. These parameters serve as the real-time control parameters for the system. After the solution is completed, the feasibility of the results can be verified. If any abnormal results exceed the physical boundaries of the equipment, the power boundaries or reserve capacity are adjusted, and the solution is recalculated.

[0033] Step S104: Real-time scheduling of the operating status of each SOFC power generation unit, DC-DC converter, and DC-AC inverter is performed using real-time control parameters.

[0034] The obtained control parameters for each time period are sent to the underlying actuators to perform closed-loop control on the operating status, output power, and thermal standby depth of each SOFC power generation unit, as well as the operating power of the DC-DC converter and DC-AC inverter, tracking the target peak-shaving command. A rolling scheduling mechanism is also employed: at the beginning of each scheduling cycle, the actual operating status, actual output power, actual thermal state of each SOFC power generation unit, and actual converter operating data are collected to update the initial state boundaries and parameters of the optimization model. The model solution and command issuance are then re-executed, forming a closed-loop scheduling process of data acquisition, optimization, execution, and feedback. By continuously updating the measured status, the thermal standby depth can be adjusted in advance based on future peak-shaving commands, reducing fuel consumption at low loads while maintaining sufficient load-up response capability, thus balancing system operating economy and dynamic response flexibility.

[0035] Specifically, this implementation method is applicable to a multi-generation unit SOFC system consisting of N SOFC power generation units, N DC-DC converters, and at least one DCAC inverter. Each SOFC power generation unit can receive fuel input and output DC power. The output of each SOFC power generation unit is connected to a DC-DC converter. The outputs of several DC-DC converters are fed into the same DCAC inverter, and the AC outputs of multiple DCAC inverters collectively meet the target peak power command.

[0036] In a typical embodiment, the topology of the SOFC power generation system is as follows: Figure 2 As shown, it consists of 10 SOFC power generation units with a rated power of 100kW, 10 DC-DC converters, and 2 DCAC inverters. SOFC-01 to SOFC-05 are connected to DCAC-01 via DC-DC-01 to DC-DC-05, and SOFC-06 to SOFC-10 are connected to DCAC-02 via DC-DC-06 to DC-DC-10. The AC outputs of the two DCACs jointly track the target peak-shaving power command at the grid connection point. This embodiment is only used to illustrate the implementation method of this method and does not limit the number or grouping of SOFC power generation units, DC-DC converters, or DCACs.

[0037] The system power link can be represented as follows: fuel consumption power Q is converted into SOFC DC output power by the SOFC power generation unit. Then, the DC-DC converter generates DC-DC output power. Multiple DC-DC outputs are fed into the DC-AC converter to form the DC-AC input power. Ultimately, the DCAC outputs AC power. The power balance constraint for optimized scheduling is set on the DCAC output side, meaning that the output power at the grid connection point or AC side satisfies the external peak-shaving command.

[0038] Optionally, step S101, which utilizes the mutual exclusion relationship of the operating states of each SOFC power generation unit during the scheduling cycle to construct the state model corresponding to each SOFC power generation unit, includes the following steps: Step S201: Obtain the operating status of each SOFC power generation unit during the scheduling cycle; wherein the operating status includes at least: normal operation status, hot standby status, shutdown process status, startup process status and cold status.

[0039] Within the time-domain framework of discrete-time scheduling, for each SOFC generator unit in the system, typical operating states covering the entire process of start-up, shutdown, steady-state operation, standby, and outage are identified and obtained. These operating states include at least five categories: normal operation, hot standby, shutdown process, start-up process, and cold state. The physical operating characteristics corresponding to each category are also clearly defined. Normal operating condition: The power generation unit maintains its rated operating temperature range, and an internal electrochemical reaction continues to occur. It can output DC power, and the output power can be dynamically adjusted within the allowable upper and lower limits. Hot standby mode: The power generation unit does not output electrical power to the outside world, but only consumes a small amount of fuel to compensate for heat loss in order to maintain the preset temperature level and retain the ability to quickly return to normal power generation mode; Shutdown process state: The transition process of the power generation unit from a high temperature state of operation or hot standby to a cold state of cooling down. During this process, no electrical power is output and there is a fixed duration constraint. Start-up process status: The transition process of the power generation unit from cold room temperature to operating temperature. No electrical power is output during the process, and there is a fixed duration constraint. Cold state: The power generation unit is in a normal temperature shutdown state, with no fuel consumption for maintaining temperature and no power output, corresponding to long-term shutdown, planned maintenance and other operating conditions.

[0040] Step S202: Determine the mutual exclusion relationship between the various states in the working state by using the operating temperature and output power of the SOFC power generation unit under normal operating conditions, the fuel consumption value and standby temperature of the SOFC power generation unit under hot standby conditions, the duration of the shutdown process state and the start-up process state, and the shutdown duration of the SOFC power generation system under cold conditions.

[0041] Based on the thermal inertia characteristics, power output attributes, and process continuity of SOFCs operating at high temperatures, and according to the temperature range, power output capacity, and steady-state / transient attributes of each state, a mutual exclusion criterion is established that a single SOFC power generation unit can belong to only one operating state within the same scheduling period. Specifically, the normal operating state is characterized by active power output and stable operating temperature, and has an essential operating boundary with the non-power output hot standby, shutdown process, startup process, and cold state; the shutdown process and startup process are unidirectional transition states, with a fixed duration limited by the rate of temperature change, and cannot coexist with the steady-state normal operation, hot standby, and cold state; the cold state corresponds to the normal temperature shutdown condition and cannot coexist with any high-temperature state. Based on the above physical operating boundaries, a logical relationship is established where the five operating states are mutually exclusive, and only one state is effective at any given time, ensuring that the state model fully matches the actual operating mechanism of the SOFC equipment.

[0042] Step S203: Construct the state model corresponding to each SOFC power generation unit based on the mutual exclusion relationship.

[0043] For the operating state of the i-th SOFC generating unit in the t-th scheduling period, corresponding 0-1 binary indicator variables are configured for the five types of operating states. The above physical mutual exclusion logic is transformed into mathematical constraints that can be incorporated into the mixed integer programming solution through variable summation constraints, and the state model of a single generating unit is constructed. This model satisfies the following relationship: (1); , , , , The variables are 0-1, representing the state of the i-th SOFC power generation unit in normal operation, hot standby, shutdown process, startup process, and cold state in the t-th time period, respectively; N is the total number of SOFC power generation units; and T is the total duration of the scheduling cycle.

[0044] When the variable takes a value of 1, it indicates that the i-th SOFC generating unit is in the corresponding operating state during time period t; when it takes a value of 0, it indicates that it is not in the corresponding operating state. N is the total number of SOFC generating units in the system, and T is the total number of time periods included in the scheduling cycle. This five-state mutual exclusion model provides a unified state logic carrier for subsequent state switching timing constraints, power boundary constraints, fuel consumption calculations, and hot standby depth constraints, and is the core foundational component of the entire multi-state optimization scheduling model.

[0045] The state and transition relationship diagram of the state model is as follows: Figure 3 As shown. Specifically, under normal operating conditions, the SOFC power generation unit operates near its operating temperature and outputs electrical power, which is limited by minimum power, maximum power, and ramp rate. In hot standby mode, the SOFC power generation unit does not output electrical power but consumes a small amount of fuel to maintain a certain temperature. Both shutdown and startup processes require several time steps, while the cold state typically corresponds to the stable state after planned maintenance, long-term shutdown, or deep shutdown.

[0046] To describe state transitions, a state transition trigger variable is introduced. and . This indicates the trigger signal for the i-th power generation unit to transition from the running / hot standby state to the shutdown process. This represents the trigger signal for the i-th generator unit to transition from a cold state to the startup process. Let the startup or shutdown delay steps be... The state evolution constraint is used to ensure that the unit must undergo a certain process after triggering a shutdown. After a period of downtime, the system enters a cold state, and upon triggering startup, it needs to undergo... The startup process takes place over a period of time before returning to the running / hot standby state.

[0047] To avoid frequent hot and cold cycles, a preferred embodiment may include an operating mode switch: when only daily peak shaving is allowed to switch between normal operation and hot standby, the operating mode is set to... When hot standby is not allowed, set For scenarios requiring planned maintenance or long-term shutdown, the shutdown process, startup process, and cold state variables can be unrestricted, and the allowed state boundaries can be specified through initial / final state constraints.

[0048] Optionally, the process of obtaining the hot standby depth includes the following steps: Step S301: Calculate the change in fuel consumption of each SOFC power generation unit in the hot standby state by using the fuel consumption of each SOFC power generation unit in the hot standby state and the hot standby baseline fuel consumption.

[0049] Pre-identifying the standby fuel consumption of each SOFC power generation unit through steady-state heat loss testing or historical operating data, the standby fuel consumption rate is obtained. This standby fuel consumption rate is the rate at which the power generation unit maintains its normal operating temperature and does not output power, used to compensate for heat loss. It serves as an energy consumption reference benchmark under standby conditions. For the i-th SOFC power generation unit in standby mode, its actual standby fuel consumption during scheduling period t is obtained. Using the standby fuel consumption rate as a reference, the difference between the benchmark value and the actual standby fuel consumption is calculated to obtain the fuel consumption change value of the corresponding power generation unit during that period. This fuel consumption change value corresponds to the amount of standby fuel saved when the standby temperature of the corresponding power generation unit is lower than the normal operating temperature. It can intuitively reflect the degree of deviation between the standby state and the full-temperature standby state, providing a calculation basis for the energy consumption dimension of the quantitative calculation of the standby depth.

[0050] Step S302: Obtain the thermal standby depth of each SOFC power generation unit based on the fuel consumption change value.

[0051] Based on the characteristic that fuel consumption for maintaining temperature and standby temperature are approximately linearly correlated within the hot standby range of SOFC high-temperature equipment, a linear mapping model between fuel consumption and hot standby depth is established. Using a preset fuel consumption variation coefficient, the aforementioned fuel consumption variation values ​​are converted into the corresponding hot standby depth. The hot standby depth is calculated using the following formula: ; The fuel consumption of the i-th SOFC power generation unit in the hot standby state during the t-th time period; The thermal standby reference fuel consumption of the i-th SOFC power generation unit; For the i-th SOFC power generation unit, the change in fuel consumption is... The variation coefficient in the figure represents the change in fuel consumption corresponding to a unit change in standby depth, and can be identified through equipment thermal characteristic tests or operating data fitting. Let be the thermal standby depth of the i-th SOFC power generation unit in the t-th time period. It is used to quantitatively characterize the degree of temperature reduction of the power generation unit relative to the normal operating temperature. The larger the thermal standby depth value, the lower the standby temperature and the less fuel is consumed to maintain the temperature, but the longer the response time required to recover from thermal standby to normal power generation state.

[0052] This linear calculation model can transform the hot standby depth into a continuously adjustable and incorporable decision variable into the optimization solution, enabling the scheduling model to quantitatively weigh the fuel economy and load increase response flexibility of hot standby, and providing a quantitative basis for subsequent hot standby depth boundary constraints, rate of change constraints and whole system optimization scheduling.

[0053] Specifically, hot standby depth , used to characterize the degree of temperature drop of the i-th SOFC power generation unit relative to its normal operating temperature during hot standby or non-operation. A greater hot standby depth results in lower fuel consumption to maintain the temperature, but also a longer recovery time from hot standby to normal operation. To avoid thermal shock and temperature cycling damage, the hot standby depth is limited by both its maximum value and rate of change: (2); (3); When the power generation unit is in normal operation, the hot standby depth is constrained to 0; when the power generation unit is in shutdown, startup, or cold state, the hot standby depth can be constrained to near the maximum hot standby depth. Fuel consumption in hot standby state is preferably expressed as a linear relationship: (4); in, Indicates the baseline fuel consumption during hot standby. This represents a coefficient indicating the variation of hot standby fuel consumption with the depth of hot standby. Under normal operating conditions, the relationship between fuel consumption and output power of an SOFC power generation unit can be obtained by fitting a quadratic function or other convex functions, preferably expressed as: (5) Since SOFCs still bear approximately fixed heat sustaining losses in the low-power range, the efficiency curve corresponding to equation (5) typically decreases significantly in the low-power range. This curve is nonlinear, which increases the difficulty of engineering solutions. To facilitate engineering solutions, the fuel consumption curve is piecewise linearized by introducing piecewise incremental variables. and piecewise slope ,get: (6) (7) Among them, the piecewise slope This represents the slope of each segment after piecewise linearization of equation (5). In actual solutions, equations (6) and (7) can be coupled with state variables through Big-M constraints or equivalent linear logic constraints, so that the power and fuel consumption in normal operation, hot standby, and other non-power generation states all satisfy the corresponding physical meanings.

[0054] Optionally, the process of obtaining the output power of the DC-DC converter includes the following steps: Step S401: Based on the quadratic function fitting relationship between each SOFC power generation unit and the corresponding DC-DC converter, obtain the quadratic fitting coefficient, the first-order fitting coefficient, and the constant fitting coefficient corresponding to the DC-DC converter.

[0055] For each SOFC generator unit and its corresponding series-connected DC-DC converter, based on measured input and output power data across the entire load range, a quadratic function is used to fit and calibrate the power mapping relationship, resulting in quadratic fitting coefficients, linear fitting coefficients, and constant fitting coefficients specific to each DC-DC converter. Since the losses of a DC-DC converter include no-load fixed losses, conduction-type linear losses, and switching-type nonlinear losses, the overall losses exhibit nonlinear characteristics with output power. The quadratic function can accurately cover the input and output characteristics under all operating conditions from light load to rated load. The fitting coefficients can be obtained through factory efficiency testing, on-site operation data identification, or parameter calibration from the equipment manual. Different specifications and individual DC-DC converters can be configured with differentiated fitting coefficients to accurately match the actual equipment characteristics. This fitting relationship is constructed with DC-DC output power as the independent variable and DC-DC input power as the dependent variable, facilitating the derivation of the required output on the SOFC side from the grid-connected target power in optimized scheduling.

[0056] Step S402: Determine the input power of the corresponding DC-DC converter based on the output power of each SOFC power generation unit.

[0057] Based on the series topology of a single SOFC generator unit corresponding to a single DC-DC converter, the DC output power of the i-th SOFC generator unit in time period t is directly used as the DC input power of the i-th DC-DC converter in the same time period, forming a one-to-one power mapping relationship. This configuration matches the single-branch series topology, where the DC output terminal of the SOFC generator unit is directly connected to the input terminal of the DC-DC converter without additional intermediate shunt or loss branches, ensuring the physical consistency between the power on the SOFC side and the power on the DC-DC input side, and providing a topological basis for the accurate calculation of subsequent converter losses.

[0058] Step S403: Calculate the output power of the DC-DC converter using the input power, quadratic fitting coefficient, first-order fitting coefficient, and constant fitting coefficient of the DC-DC converter.

[0059] Substituting the input power of the DC-DC converter with the pre-calibrated quadratic fitting coefficients, linear fitting coefficients, and constant fitting coefficients into the preset quadratic function relationship, the output DC power of the corresponding DC-DC converter is calculated, specifically satisfying the following formula: ; Let be the output power of the i-th DC-DC converter in the t-th time period; Let be the input power of the i-th DC-DC converter in the t-th time period, which corresponds to the output power of the i-th SOFC generator unit. For the i-th DC-DC converter, the quadratic fitting coefficients correspond to the switching loss components that increase with the square of the power. For the first-order fitting coefficient of the i-th DC-DC converter, there are the conduction loss components that increase linearly with power. represents the constant fitting coefficients of the i-th DC-DC converter, corresponding to the no-load fixed loss components.

[0060] This quadratic function is a monotonically non-decreasing convex function within the normal operating power range of the DC-DC converter. It can be transformed into a constraint form that can be handled by mixed-integer linear programming through piecewise linearization and incorporated into the overall system optimization scheduling model. By accurately characterizing the power loss characteristics of the DC-DC converter, the low-load calculation deviation caused by uniform efficiency calculation in traditional scheduling can be avoided. This enables a precise physical mapping between the SOFC side output and the DC bus side power, improving the engineering feasibility of the scheduling results.

[0061] Optionally, the process of obtaining the output power of the DCAC inverter includes the following steps: Step S501: Obtain the efficiency curve of the DCAC inverter using the historical operating data of the SOFC power generation system, and use the efficiency curve to obtain the conversion efficiency of the DCAC inverter.

[0062] Step S502: Determine the input power of the DCAC inverter based on the power accumulation results of all DC-DC converters.

[0063] Step S503: Calculate the output power of the DCAC inverter using the input power and conversion efficiency.

[0064] The output power of the DCAC inverter is calculated using the following formula: ; Let g be the output power of the g-th DCAC inverter in the t-th time period; Let be the input power of the g-th DCAC inverter in the t-th time period; Let be the conversion efficiency of the g-th DC-AC inverter in the t-th time period.

[0065] To ensure that the optimized scheduling results directly correspond to the actual available power on the grid-connected side, the efficiency curves of the DC-DC and DC-AC converters are incorporated into the main optimization model. For the i-th DC-DC converter, let its output power be... Its input power corresponds to the SOFC output power. The input-output power relationship is preferably described using a quadratic relation: (8) (9) For the g-th DC-AC inverter, let its AC output power be... The input power is Then we have: (10) (11) (12) (13) in, This represents the set of SOFC-DCDC branches connected to the g-th DCAC. This represents the target peak power command for the t-th time period. If DCAC efficiency is not considered in a certain engineering scenario, the power balancing can be set on the DC-DC output side; if the efficiency of DC-DC and DCAC is not considered, it can be degenerated into SOFC output side power balancing. The preferred main scheme is the SOFC-DCDC-DCAC joint optimization scheduling model shown in equations (8) to (13).

[0066] In engineering implementation, the secondary input-output relationships of SOFC, DCDC, and DCAC can be identified through test points or operational data. Since equations (8) and (10) are generally convex and monotonically non-decreasing functions within the operating range, a piecewise linearization method consistent with the SOFC fuel curve can be adopted, introducing piecewise incremental variables for DCDC. and DCAC piecewise incremental variables This transforms the converter efficiency relationship into a linear constraint that can be handled by MILP.

[0067] Optionally, the constraints for constructing a mixed-integer optimization model based on the target peak-shaving power of the SOFC power generation system include the following steps: Step S601: Construct grid-connected power balance constraints using the target peak-shaving power of the SOFC power generation system and the total output power of the DCAC inverter.

[0068] With power supply and demand matching at the grid connection point as the core, the power balance constraint of the total system output is set on the AC output side of the DCAC inverter, constructing a grid-connected power balance constraint: in each time period within the scheduling cycle, the sum of the AC output power of all DCAC inverters is equal to the target peak-shaving power command corresponding to that time period. This constraint directly anchors external peak-shaving demand to the final grid-connected output, automatically covering the full-link power loss of the DC-DC converter and DCAC inverter, avoiding the actual grid-connected output deviation problem caused by setting the power balance on the SOFC side in traditional models, and ensuring that the optimized scheduling results can directly meet the peak-shaving power requirements of the grid side.

[0069] Step S602: Construct the state mutual exclusion constraints of the SOFC power generation unit through the working state mutual exclusion relationship of the state model.

[0070] Based on the five-state partitioning of SOFC power generation units, a state mutual exclusion constraint is constructed using 0-1 state variables: for each scheduling period of each SOFC power generation unit, the sum of the binary variables corresponding to the five states—normal operation, hot standby, shutdown process, startup process, and cold state—is always equal to 1. This constraint mathematically ensures that a single power generation unit can only be in one operating state at any given time, eliminating the logical contradiction of multiple states coexisting. This ensures that the state model fully matches the physical operating laws of the SOFC equipment, providing a logical basis for the subsequent coupling of power, energy consumption, and thermal characteristic constraints with state variables.

[0071] Step S603: Construct the state switching timing constraints of the SOFC power generation unit using the state switching parameters of the SOFC power generation unit in the state model.

[0072] By introducing state transition trigger variables and start / stop delay parameters, timing constraints for state transitions are constructed to standardize the jump paths and transition durations between different states. Specifically, a shutdown trigger variable is set to detect when the power generation unit enters the shutdown process from the running / hot standby side, and a start trigger variable is set to detect when the power generation unit enters the start-up process from the cold state. Combined with preset start / stop delay steps, the shutdown process must last for a specified duration before transitioning to the cold state, and the start-up process must last for a specified duration before transitioning to normal operation or hot standby state, prohibiting instantaneous jumps between different steady states by the power generation unit. This constraint fully matches the thermal inertia characteristics of SOFC high-temperature equipment, avoids state transitions that do not conform to temperature change patterns, and reduces the wear and tear on equipment lifespan caused by thermal cycling shocks.

[0073] Step S604: Construct SOFC power boundary constraints and power ramping constraints by using the output power range and power change value of each SOFC power generation unit under normal operating conditions.

[0074] For the output characteristics of SOFC power generation units, steady-state power boundary constraints and dynamic power ramp-up constraints are constructed respectively. The power boundary constraints stipulate that when the power generation unit is in normal operation, its output power must be between the minimum technical output and the rated maximum power; when the power generation unit is in non-power generation states such as hot standby, shutdown process, startup process, or cold state, the output power is forced to 0; this constraint can be coupled with state variables through Big-M logic constraints to ensure the logical consistency between state and power.

[0075] Power ramping constraint: Between two adjacent scheduling periods, the absolute value of the change in output power of the SOFC power generation unit shall not exceed the maximum ramping rate allowed by the equipment; this constraint matches the dynamic response capability of the SOFC stack and thermal management system to avoid sudden temperature changes and instability caused by drastic power fluctuations.

[0076] Step S605: Construct thermal standby depth constraints by using the thermal standby depth values ​​and their rate of change for each SOFC power generation unit.

[0077] To address the adjustable range and adjustment rate of the standby depth, two types of standby depth constraints are constructed: First, a boundary constraint on the standby depth value, limiting the standby depth of a single power generation unit to between 0 and the maximum allowable standby depth, with the standby depth forcibly set to 0 when the power generation unit is in normal operation. Second, a constraint on the rate of change of the standby depth, limiting the absolute value of the change in standby depth between adjacent scheduling periods to not exceed the maximum allowable rate of change. This set of constraints ensures that the standby depth adjustment remains within the equipment's temperature tolerance range, avoids thermal stress damage by limiting the rate of temperature change, and makes the standby depth a continuously optimizable variable, supporting a quantitative trade-off between fuel economy and load increase response speed.

[0078] Step S606: Construct piecewise linearization constraints for SOFC fuel consumption, DC-DC efficiency, and DCAC efficiency using the piecewise linearization results of the SOFC fuel consumption curve, DC-DC efficiency curve, and DCAC efficiency curve of the SOFC power generation system, respectively.

[0079] To address the nonlinear convex characteristics of the SOFC fuel consumption curve, DC-DC efficiency curve, and DCAC efficiency curve, piecewise linearization is applied to each curve, resulting in three types of piecewise linearization constraints: SOFC fuel consumption piecewise linearization constraint: By introducing piecewise incremental variables and piecewise slope parameters, the quadratic convex function relationship between output power and fuel consumption under normal operating conditions is transformed into a multi-segment linear constraint. Through the coupling of logical constraints and operating state variables, accurate calculation of fuel consumption under different conditions is achieved. Piecewise linearization constraint for DCDC efficiency: The quadratic relationship between the input power and output power of the DCDC converter is decomposed into multiple linear mappings, so that the change law of converter loss with load can be expressed by linear constraints. Piecewise linearization constraints for DCAC efficiency: Similarly, the AC / DC power relationship of the DCAC inverter is transformed into piecewise linear constraints. Through this set of constraints, the nonlinear efficiency characteristics of the entire link can be uniformly incorporated into the solution of the mixed-integer linear programming model, which significantly improves the solution efficiency and stability of the model while ensuring engineering accuracy.

[0080] Step S607: Construct DCAC grouping bus constraints by using the input power of the DCAC inverter and the total output power of all connected DC-DC converters.

[0081] Based on the system's grouped topology, a DCAC grouping constraint is constructed: for each DCAC inverter, its DC-side input power is equal to the sum of the output power of all DC-DC converters connected to that inverter. This constraint corresponds to the physical process of power combining on the DC bus, establishing a power mapping relationship between the output of a single branch DC-DC converter and the group-level DCAC input. It serves as an intermediate link connecting the characteristics of a single generator unit with the total grid-connected output of the system, ensuring the self-consistency and physical consistency of the power flow throughout the system.

[0082] Optionally, the objective function corresponding to the decision objective is: ; in, The output value of the objective function; Let be the fuel consumption of the i-th SOFC power generation unit in the t-th time period; Let i represent the shutdown process state of the i-th SOFC power generation unit during the t-th time period. Let be the fuel consumption of the i-th SOFC power generation unit during the shutdown process. The shutdown cost of the i-th SOFC power generation unit in the t-th time period; Let i represent the startup process state of the i-th SOFC power generation unit in the t-th time period. Let be the fuel consumption of the i-th SOFC power generation unit during the startup process. This represents the startup cost of the i-th SOFC power generation unit in the t-th time period. The above coefficients can be adjusted based on actual equipment start-up and shutdown fuel consumption, lifespan depreciation costs, or operation and maintenance costs, and can be uniformly converted into fuel consumption based on these costs.

[0083] The main constraints in this embodiment include: 1. State mutual exclusion constraint: Each SOFC power generation unit can only be in one of the following states at any given time period: normal operation, hot standby, shutdown process, startup process, and cold state; 2. State transition timing constraints: through , and Describe the transition relationships between the shutdown process, cold state, startup process, and running / hot standby states; 3. SOFC power boundary constraints: Under normal operating conditions, the output power is within the specified range. and Between these conditions, the output power is 0 when not generating electricity; 4. Power ramp-up constraint: The absolute value of the SOFC output power change between adjacent time periods shall not exceed ; 5. Hot standby depth constraint: The hot standby depth and its rate of change are subject to an upper limit constraint. Under normal operating conditions, the hot standby depth is 0. 6. Piecewise linearization constraint for SOFC fuel consumption: The convex fuel consumption curve is represented by piecewise incremental variables; 7. Piecewise linearization constraint for DC-DC efficiency: The required output power of SOFC is calculated by back-calculating the output power of DC-DC. 8. Piecewise linearization constraint for DCAC efficiency: Inversely deduce DCAC input power from DCAC output power; 9. DCAC group bus constraint: The input power of each DCAC is equal to the sum of the output power of the DC-DC converters connected to it; 10. Grid-connected power balance constraint: The sum of all DCAC output power equals the target peak-shaving power command; 11. Initial and final state boundary constraints: Specify the state and hot standby depth of each unit at the beginning and end of the scheduling cycle according to the engineering plan.

[0084] Optionally, the real-time control parameters corresponding to the decision variables when the mixed-integer optimization model satisfies the decision objective under constraints are obtained, including the following steps; Step S701: Solve for the objective decision variables corresponding to the mixed integer optimization model with the decision objective in mind.

[0085] Under all preset constraints, the mixed-integer optimization model is solved with the goal of minimizing the total fuel consumption and start-up / shutdown costs of the SOFC power generation system within the scheduling cycle. The optimal solutions for all decision variables in each scheduling period are obtained, i.e., the target decision variables. The solution process can be implemented using a mature commercial mixed-integer programming solver, ensuring computational accuracy while also meeting the real-time requirements of scheduling, and outputting variable optimization results covering the entire scheduling time domain.

[0086] Step S702: Based on the target decision variables, obtain one or more real-time control parameters among the target operating status, target output power, target fuel consumption, target thermal standby depth, target DC-DC converter output power, and target DC-AC inverter output power of each SOFC power generation unit.

[0087] Based on the target decision variables obtained from the solution, the corresponding operational control quantities of each device are extracted to form real-time control parameters for execution. These real-time control parameters may include the target operating state, target output power, target fuel consumption, and target thermal standby depth of each SOFC power generation unit, as well as one or more of the target output power of the corresponding DC-DC converter and DC-AC inverter. These parameters cover the entire control requirements of the power generation unit's operating state, active power output, thermal state, and power electronic conversion links, and can be directly used as the basis for scheduling instructions from the underlying execution mechanism.

[0088] Step S703: If the output power of each SOFC generator unit, DC-DC converter or DC-AC inverter under the real-time control parameters does not meet the power boundary conditions corresponding to the constraint conditions, then update the real-time control parameters based on the output power until the output power meets the power boundary conditions.

[0089] The extracted real-time control parameters are subjected to feasibility verification. The output power of each SOFC generator unit, DC-DC converter, and DC-AC inverter is checked against the power boundary requirements defined by the constraints. If the output power of any device exceeds the corresponding power boundary, the input parameters and constraints of the optimization model are adjusted based on the power boundary limit. The model is then re-solved, and the real-time control parameters are updated. Through iterative adjustment and verification, the output power of all devices meets the preset power boundary conditions, ensuring that the final output control commands match the actual operating capacity of the equipment and avoiding unexecutable scheduling commands.

[0090] like Figure 4 The flowchart shown represents another scheduling and control method for SOFC power generation systems. This method can be executed on a rolling basis at fixed cycles, with typical scheduling time steps of 5 minutes, 15 minutes, 30 minutes, or 1 hour, and the scheduling time domain can cover 24 hours or longer. This method can be executed by a host computer, energy management system, power plant controller, or embedded controller. The specific implementation steps are as follows: 1. Input acquisition, P ref Initial and final states, equipment parameters. Acquire external total peak power command (P... ref The current operating status, current output power, current temperature or current hot standby depth, current fuel consumption, current DC-DC output power, current DC-AC output power, and equipment boundary parameters of each SOFC power generation unit; 2. Parameter identification: SOFC fuel curve, DC-DC / DCAC efficiency curve. Identify the SOFC power-fuel consumption curve, hot standby fuel consumption relationship, DC-DC input-output power curve, and DCAC input-output power curve based on experimental data or historical operating data. 3. Establish the model with five-state variables and hot standby depth. Establish a five-state SOFC generator unit model within the scheduling time domain, and set the initial state, terminal state, power boundary, ramp rate, hot standby depth boundary, and start / stop delay. 4. Model linearization. The SOFC fuel consumption curve, DC-DC efficiency curve, and DCAC efficiency curve are piecewise linearized and coupled with state variables through Big-M constraints or equivalent linear constraints; 5. MILP solution, minimum fuel consumption, start-up and shutdown costs. The MILP is solved with the goal of minimizing total fuel consumption and start-up and shutdown costs to obtain the target operating state, target output power, target thermal standby depth, target fuel consumption, target DC-DC output power, and target DC-AC output power for each SOFC power generation unit. 6. Verification and correction: feasibility, boundaries, and ramp limits. Perform a feasibility check on the solution results. If any exceedances of power boundaries, ramp limits, or state boundaries are found, adjust the peak-shaving command, reserve capacity, or state boundaries and solve again. 7. Output commands, unit status, power, and hot standby depth. Output scheduling commands to the underlying control system; 8. Execute the above steps in a rolling fashion, update the measured status, and proceed to the next cycle. In the next scheduling cycle, continuously update and optimize the model based on the measured status.

[0091] This method performs optimal scheduling in the discrete time domain. In each scheduling cycle, the controller obtains the target peak power command, the initial state of each SOFC power generation unit, the expected state at the end, the upper and lower power limits, the power ramping capability, the fuel consumption curve, the hot standby fuel consumption parameters, the hot standby depth variation capability, the DC-DC efficiency curve and the DC-AC efficiency curve, and constructs a mixed integer linear programming (MILP) model based on these parameters.

[0092] The MILP model uses the operating status, output power, fuel consumption, hot standby depth, DC-DC output power, and DC-AC output power of each SOFC power generation unit in each time period as decision variables. With the goal of minimizing total fuel consumption and start-up / shutdown costs throughout the entire scheduling cycle, the model solves for the optimal state sequence and power allocation sequence of each power generation unit under the constraints of multi-state switching, hot standby depth, power ramping, power balance, and converter efficiency.

[0093] Compared to a simple power-sharing strategy, this method does not require all SOFC generating units to uniformly bear the output during low loads. Instead, it allows some units to switch to hot standby or other non-generating states, ensuring that generating units operate within their high-efficiency range as much as possible. Simultaneously, this method limits the rate of change of hot standby depth, enabling units to adjust their thermal state in advance according to future peak-shaving commands, reducing fuel consumption while retaining a certain load-up response capability.

[0094] To verify the effectiveness of this scheme, a megawatt-level multi-generator unit SOFC system was used for simulation analysis. The system includes 10 SOFC generator units, each with a rated power of 100kW; each SOFC generator unit is connected to one DC-DC converter; every 5 DC-DC converters are connected to one DCAC inverter, for a total of 2 DCAC inverters.

[0095] The fuel consumption curve of the SOFC power generation unit is set as a convex function that monotonically increases with output power. The efficiency curve decreases rapidly in the low power range and tends to stabilize in the higher power range. To simulate differences in actual equipment, the 10 SOFC power generation units can be set with non-identical fuel consumption curves, so that some units consume slightly more fuel at the same power. A linear relationship is adopted between hot standby fuel consumption and hot standby depth, and the rate of change of hot standby depth is constrained by temperature change.

[0096] External peak-shaving commands can use a 24-hour time domain and a 15-minute time step load curve, or a typical dynamic command that first linearly decreases from 1000kW to 200kW, maintains low load for a period of time, and then increases back to 1000kW, in order to examine the system's efficiency and flexibility during low load and load increase / decrease processes.

[0097] First, consider the steady-state optimal power allocation result. The optimization yields the system's fuel consumption, efficiency, average power of power generation units, and number of power generation units activated under different power levels, as shown below. Figure 5 As shown. Among them. Figure 5 The percentage represents the proportion of fuel consumption in hot standby mode to fuel consumption when power is 0, which can be achieved by adjusting the hot standby depth. Figure 5 (a) in the figure represents the fuel consumption rate of the system under different loads, expressed in terms of low calorific value; Figure 5 (b) in the figure represents the power generation efficiency of the system under different loads; Figure 5 In this context, (c) represents the number of units that are powered on under different system loads; Figure 5 In this context, (d) represents the average load of the powered-on unit under different system loads.

[0098] In the steady-state example, when the total system output is high, all SOFC power generation units are in normal operation, and the units themselves operate in a high-efficiency range, so the efficiency improvement brought by hot standby scheduling is small. When the total system output decreases, if the equal-distribution strategy is still adopted, each SOFC power generation unit will operate in a low-efficiency range, and the total system fuel consumption will increase significantly.

[0099] After adopting the optimized scheduling method proposed in this scheme, the model will automatically select some units to enter the hot standby state based on the fuel consumption curve and hot standby fuel consumption, so that the remaining normally operating units can bear a higher average load and remain in the high efficiency range. Simulation results show that as the hot standby energy consumption decreases, the number of modules activated during low load periods decreases, fuel consumption decreases, and efficiency gradually increases, while the system operating characteristics during high load periods are basically unaffected by hot standby energy consumption. Under low load conditions where the total system output is approximately 20% of the rated power, optimized scheduling can improve the system's steady-state efficiency by about 7 percentage points. The average power of the generating units in the operating state during low load periods also gradually increases with the decrease in hot standby energy consumption. This characteristic can be explained by the efficiency characteristic curve of the SOFC generating units. From the efficiency and energy consumption characteristic curves, it can be seen that when the load of the generating unit is below 50% of the rated load, the efficiency decreases rapidly. If the hot standby state can reduce energy consumption, the system will tend to have some generating units operate in the hot standby state, while concentrating the load on a few operating generating units, thereby improving the overall system operating efficiency. The lower the energy consumption of hot standby, the lower the cost of hot standby, and therefore the more power generation units can be in hot standby mode, leading to a higher average load on the operating units. This indicates that the proposed scheduling method can not only maintain high system efficiency under low load conditions, but also create conditions for the system to achieve deeper low-load peak shaving by reducing the number of inefficient operating units and increasing the load level of the remaining units.

[0100] In the above simulation, since only one time period was considered, the simulation results cannot reflect the time delay of the system recovering from hot standby to normal operation. Therefore, the following simulation assumes 24 time periods, that is, it considers the optimized scheduling within one day. The target power curve first linearly decreases from 1000kW to 200kW, maintains 200kW for 5 hours, and then linearly increases back to 1000kW. This scenario corresponds to the system's minimum operating power dropping to 20% of the rated power, which can be used to verify the deep peak-shaving capability of the proposed method. Since SOFC stacks cannot withstand frequent hot and cold cycles, the modules are only allowed to enter a shutdown state when periodic maintenance is required. Therefore, additional constraints are introduced in this simulation to ensure that the power generation unit does not enter a shutdown state. The dynamic simulation results are as follows: Figure 6 As shown.

[0101] Figure 6 Optimized scheduling scheme for megawatt-level systems under the condition of consistent power generation units. Figure 6 In this context, (a) represents the system's fuel consumption rate and output power; Figure 6 (b) in the figure represents the system power generation efficiency; Figure 6 (c) in the text represents the load of each power generation unit; Figure 6 In this context, (d) represents the hot standby depth of each power generation unit; Figure 6 (e) in the text indicates the operating status of each power generation unit; Figure 6 (f) in the figure represents the hot standby status of each power generation unit.

[0102] Simulation results show that as the power command decreases, the power output of the generating units gradually decreases, eventually entering a hot standby state to reduce energy consumption after reaching a certain level. The variation in hot standby depth demonstrates that the proposed method can effectively achieve multi-unit scheduling under different total power commands. Furthermore, during the process of the system's total output dropping to 200kW and maintaining this state for 5 hours, some generating units enter hot standby states at different depths. The system can achieve stable operation at 20% rated load without a complete shutdown, indicating that the proposed strategy has good deep peak-shaving capabilities. Simultaneously, since the hot standby units remain near their operating temperature, reducing energy consumption only by adjusting the hot standby depth, when the system load rises again, the relevant units can quickly recover from the hot standby state to normal power generation without undergoing a complete cold start process. This effectively improves the response speed during the load recovery phase, reflecting the operational characteristic of exchanging partial hot standby for rapid recovery. Therefore, this strategy not only achieves efficient regulation during load decline but also meets the needs of rapid load ramp-up after a low load.

[0103] For inconsistent modules, assuming energy consumption gradually increases from power generation unit 1 to power generation unit 10, the simulation results are as follows. It can be observed that different power generation units no longer follow the principle of equal power; modules with lower efficiency have lower power and enter hot standby state first, and are also the last to exit hot standby state when power is increased. This demonstrates that the proposed optimization model can effectively consider the differences between different modules and achieve optimal power allocation.

[0104] 24-hour optimized dispatch scheme for megawatt-level systems under inconsistent generation units, such as Figure 7 As shown, Figure 7 In this context, (a) represents the system's fuel consumption rate and output power; Figure 7 (b) in the figure represents the system power generation efficiency; Figure 7 (c) in the text represents the load of each power generation unit; Figure 7 In this context, (d) represents the hot standby depth of each power generation unit; Figure 7 (e) in the text indicates the operating status of each power generation unit; Figure 7 (f) in the figure represents the hot standby status of each power generation unit.

[0105] Furthermore, this differentiated allocation strategy not only reflects the principle of efficiency priority but also embodies the idea of ​​lifetime-friendly scheduling. For generating units with lower efficiency and higher aging levels, the model tends to allocate lower operating loads and prioritize putting them into hot standby mode; while for units with higher efficiency and better health, they undertake more output tasks. Through this load sharing method of high-efficiency units generating more power and low-efficiency units generating less power, the system's power demand can be met while avoiding long-term high-load operation of inefficient and aging units, thereby mitigating their further degradation to some extent and improving the overall lifetime balance of the multi-unit system. Therefore, the proposed method can take into account both the differences in unit aging and lifetime maintenance requirements during long-term system operation.

[0106] Building upon this, the applicability of the method to 15-minute / 96-point dispatch commands in actual power grid peak-shaving scenarios was further tested. The figure below illustrates a typical optimized dispatch strategy for a megawatt-scale system under conditions where hot standby is not permitted. It can be observed that when hot standby is not allowed, the load of each generating unit follows a pattern of high-efficiency units receiving higher loads and low-efficiency units receiving lower loads. Because the efficiency of generating units drops sharply in low-load intervals, the system efficiency also decreases rapidly around the 20th and 60th dispatch commands, reaching a minimum of only about 47%.

[0107] When hot standby is not allowed, the optimized scheduling scheme for a megawatt-level system with 15 minutes / 96 points is as follows: Figure 8 As shown, Figure 8 In this context, (a) represents the system's fuel consumption rate and output power; Figure 8 (b) in the text represents the load of each power generation unit; Figure 8 In this context, (c) represents system efficiency; Figure 8 In this context, (d) represents the hot standby depth of each power generation unit.

[0108] In contrast, the multi-state optimization scheduling strategy for power generation units proposed in this paper allows some power generation units to be shut down during low-load periods, consuming a small amount of fuel to keep their no-load operation near their operating temperature, thereby enabling the powered generation units to operate in their high-efficiency range and thus improving the overall system operating efficiency. In comparison with... Figure 7 Under the same dispatch instructions, the optimized dispatch scheme when hot standby is allowed is shown in the figure below. It can be seen that when hot standby is allowed, inefficient power generation units during low-load periods will be prioritized for hot standby operation, and the hot standby depth is maximized, thereby reducing the fuel consumption required to maintain the temperature. In this case, less than half of the power generation units are in power generation mode, operating in the high-efficiency range, effectively improving system efficiency to over 61.5%.

[0109] Optimized scheduling scheme for megawatt-level systems with hot standby capability, 15 minutes / 96 points, such as... Figure 9 As shown, Figure 9 In this context, (a) represents the system's fuel consumption rate and output power; Figure 9 (b) in the text represents the load of each power generation unit; Figure 9 In this context, (c) represents system efficiency; Figure 9 In this context, (d) represents the hot standby depth of each power generation unit.

[0110] contrast Figure 8 and Figure 9 To further illustrate, the advantages of the multi-state optimized scheduling strategy are not only reflected in efficiency improvement, but also in several aspects such as peak shaving depth, response speed, and lifespan protection: First, during low-load periods, the system can achieve more flexible output voltage reduction through deep hot standby of some units, supporting deeper-level peak shaving operation; second, during the subsequent load recovery phase, units in partial hot standby can quickly exit hot standby and resume normal power generation, providing the system with better dynamic response capabilities; third, by prioritizing the reduction of the load of inefficient units and delaying their re-entry into operation, it helps to alleviate the operating burden of heavily aging units, demonstrating the scheduling optimization effect for long-term lifespan maintenance.

[0111] As can be seen from the above comparison, this embodiment is particularly suitable for megawatt-level SOFC power generation systems with frequent changes in peak shaving commands, long periods of low-load operation, a large number of SOFC units, and non-negligible power electronic link losses. This embodiment can automatically select some SOFC power generation units to enter a hot standby state during low load periods, keeping other units in a high-efficiency operating range. Simultaneously, through constraints on the depth of hot standby and prediction of future peak shaving commands, it avoids insufficient load increase response capability due to excessive hot standby. By incorporating the efficiency curves of the DC-DC and DC-AC converters into the optimization model, this scheme can also maintain a consistent physical mapping relationship between the SOFC-side output power, DC-side power, and grid-connected power, improving the engineering feasibility of the scheduling results.

[0112] As can be seen from the above-mentioned scheduling and control method for SOFC power generation systems, this method unifies the working status, hot standby depth, start-stop delay, fuel consumption curve, DC-DC efficiency curve, DC-AC efficiency curve, and grid-connected peak-shaving power command of each SOFC power generation unit into a mixed integer optimization model. Under the premise of ensuring peak-shaving power tracking capability and equipment operation constraints, it reduces fuel consumption, improves low-load operation efficiency, and takes into account the system's dynamic response flexibility.

[0113] Corresponding to the above-described embodiments of the dispatch control method for SOFC power generation systems, this invention also provides a dispatch control device for SOFC power generation systems. The SOFC power generation system includes multiple SOFC power generation units, DC-DC converters connected to each SOFC power generation unit, and at least one DC-AC inverter. Figure 10As shown, the device includes: State model construction module 100: used to construct the state model corresponding to each SOFC power generation unit by utilizing the mutual exclusion relationship of the working states of each SOFC power generation unit during the scheduling cycle. Mixed Integer Optimization Model Construction Module 200: This module is used to construct a mixed integer optimization model with the operating state, output power, fuel consumption, thermal standby depth, DC-DC output power, and DC-AC output power of each SOFC power generation unit in the state model as decision variables, and with the goal of minimizing the total fuel consumption and start-up / shutdown cost of the SOFC power generation system within the scheduling cycle. Real-time control parameter acquisition module 300: After constructing the constraint conditions of the mixed integer optimization model based on the target peak-shaving power of the SOFC power generation system, it acquires the real-time control parameters corresponding to the decision variables when the mixed integer optimization model satisfies the decision objective under the constraint conditions. Real-time scheduling and control module 400: Used to schedule the working status of each SOFC power generation unit, DC-DC converter and DC-AC inverter in real time through real-time control parameters.

[0114] As can be seen from the above-mentioned dispatch and control device for SOFC power generation system, the device integrates the working status, hot standby depth, start-stop delay, fuel consumption curve, DC-DC efficiency curve, DC-AC efficiency curve and grid-connected peak-shaving power command of each SOFC power generation unit into a unified mixed integer optimization model. Under the premise of ensuring peak-shaving power tracking capability and equipment operation constraints, it reduces fuel consumption, improves low-load operation efficiency, and takes into account the system's dynamic response flexibility.

[0115] The scheduling and control device for SOFC power generation system provided in this embodiment of the invention has the same implementation principle and technical effect as the aforementioned scheduling and control method for SOFC power generation system. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned scheduling and control method for SOFC power generation system.

[0116] This embodiment also provides an electronic device, the structural schematic diagram of which is shown below. Figure 11 As shown, the device includes a processor 101 and a memory 102; wherein, the memory 102 is used to store one or more computer instructions, which are executed by the processor to implement the steps of the dispatch control method for SOFC power generation system described above.

[0117] Figure 11 The electronic device shown also includes a bus 103 and a communication interface 104, with the processor 101, communication interface 104 and memory 102 connected via the bus 103.

[0118] The memory 102 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device. The bus 103 may be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 11 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0119] The communication interface 104 is used to connect to at least one personnel terminal and other network units through a network interface, and to send encapsulated IPv4 packets or IPv4 packets to the personnel terminal through the network interface.

[0120] Processor 101 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 101 or by instructions in software form. The processor 101 may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this disclosure. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this disclosure can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory 102. The processor 101 reads the information in memory 102 and, in conjunction with its hardware, completes the steps of the method described in the foregoing embodiments.

[0121] This invention also provides a storage medium storing a computer program, which, when executed by a processor, performs the steps of the scheduling and control method for SOFC power generation systems described in the foregoing embodiments.

[0122] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, devices, and methods can be implemented in other ways. The system embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0123] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0124] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0125] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, electronic device, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0126] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A dispatch control method for SOFC power generation systems, characterized in that, The SOFC power generation system includes multiple SOFC power generation units, a DC-DC converter connected to each SOFC power generation unit, and at least one DC-AC inverter; the method includes: By utilizing the mutual exclusion relationship of the working states of each SOFC power generation unit during the scheduling cycle, a state model corresponding to each SOFC power generation unit is constructed. Using the operating state, output power, fuel consumption, hot standby depth, DC-DC converter output power, and DC-AC inverter output power of each SOFC power generation unit in the state model as decision variables, a mixed integer optimization model is constructed with the goal of minimizing the total fuel consumption and start-up / shutdown cost of the SOFC power generation system within the scheduling cycle. After constructing the constraints of the mixed integer optimization model based on the target peak-shaving power of the SOFC power generation system, the real-time control parameters corresponding to the decision variables are obtained when the mixed integer optimization model satisfies the decision objective under the constraints. The operating status of each SOFC power generation unit, the DC-DC converter, and the DC-AC inverter is scheduled in real time using the real-time control parameters.

2. The dispatch control method for SOFC power generation systems according to claim 1, characterized in that, The steps of constructing a state model for each SOFC power generation unit by utilizing the mutual exclusion relationship of the operating states of each SOFC power generation unit during the scheduling cycle include: The operating status of each SOFC power generation unit during the scheduling cycle is obtained; wherein, the operating status includes at least: normal operation status, hot standby status, shutdown process status, startup process status, and cold state. The mutual exclusion relationship between the various states in the working state is determined by the operating temperature and output power of the SOFC power generation unit under normal operating conditions, the fuel consumption value and standby temperature of the SOFC power generation unit under hot standby conditions, the duration of the shutdown process state and the startup process state, and the shutdown duration of the SOFC power generation system under cold conditions. Based on the mutual exclusion relationship, a state model is constructed for each SOFC power generation unit; wherein, the state model satisfies the following relationship: ； , , , , The variables are 0-1, representing the i-th SOFC power generation unit in normal operation, hot standby, shutdown process, startup process, and cold state in the t-th time period, respectively; N is the total number of SOFC power generation units; and T is the total duration of the scheduling cycle.

3. The dispatch control method for SOFC power generation systems according to claim 1, characterized in that, The process of obtaining the hot standby depth includes: The change in fuel consumption of each SOFC power generation unit in the hot standby state is calculated by comparing the fuel consumption of each SOFC power generation unit in the hot standby state with the hot standby baseline fuel consumption. The thermal standby depth of each SOFC power generation unit is obtained based on the fuel consumption change value; wherein, the thermal standby depth is calculated by the following formula: ； The fuel consumption of the i-th SOFC power generation unit in the hot standby state during the t-th time period; The thermal standby reference fuel consumption of the i-th SOFC power generation unit; For the i-th SOFC power generation unit, the change in fuel consumption is... The coefficient of variation in; The thermal standby depth of the i-th SOFC power generation unit during the t-th time period.

4. The dispatch control method for SOFC power generation systems according to claim 1, characterized in that, The process of obtaining the output power of the DC-DC converter includes: Based on the quadratic function fitting relationship between each SOFC power generation unit and the corresponding DC-DC converter, the quadratic fitting coefficient, the first-order fitting coefficient, and the constant fitting coefficient of the DC-DC converter are obtained. The input power of the corresponding DC-DC converter is determined based on the output power of each SOFC power generation unit. The output power of the DC-DC converter is calculated using the input power, the quadratic fitting coefficient, the first-order fitting coefficient, and the constant fitting coefficient; wherein, the output power of the DC-DC converter is calculated using the following formula: ； Let be the output power of the i-th DC-DC converter in the t-th time period; Let be the input power of the i-th DC-DC converter in the t-th time period; The quadratic fitting coefficients are those of the i-th DC-DC converter; The first-order fitting coefficients of the i-th DC-DC converter; The constant fitting coefficients are those of the i-th DC-DC converter.

5. The dispatch control method for SOFC power generation systems according to claim 1, characterized in that, The process of obtaining the output power of the DCAC inverter includes: The efficiency curve of the DCAC inverter is obtained using the historical operating data of the SOFC power generation system, and the conversion efficiency of the DCAC inverter is obtained using the efficiency curve. The input power of the DCAC inverter is determined based on the power accumulation result of all DC-DC converters. The output power of the DCAC inverter is calculated using the input power and the conversion efficiency; wherein, the output power of the DCAC inverter is calculated using the following formula: ； Let g be the output power of the g-th DCAC inverter in the t-th time period; Let be the input power of the g-th DCAC inverter in the t-th time period; Let be the conversion efficiency of the g-th DC-AC inverter in the t-th time period.

6. The dispatch control method for SOFC power generation systems according to claim 1, characterized in that, The objective function corresponding to the decision objective is: ； in, The output value of the objective function; Let be the fuel consumption of the i-th SOFC power generation unit in the t-th time period; Let i represent the shutdown process state of the i-th SOFC power generation unit during the t-th time period. Let be the fuel consumption of the i-th SOFC power generation unit during the shutdown process. The shutdown cost of the i-th SOFC power generation unit in the t-th time period; Let i represent the startup process state of the i-th SOFC power generation unit in the t-th time period. Let be the fuel consumption of the i-th SOFC power generation unit during the startup process. The startup cost of the i-th SOFC power generation unit in the t-th time period.

7. The dispatch control method for SOFC power generation systems according to claim 1, characterized in that, The constraints for constructing the mixed-integer optimization model based on the target peak-shaving power of the SOFC power generation system include: The grid-connected power balance constraints are constructed by using the target peak-shaving power of the SOFC power generation system and the total output power of the DCAC inverter. The mutually exclusive state constraints of the SOFC power generation unit are constructed by the mutually exclusive relationship of the working state of the state model. The state switching timing constraints of the SOFC power generation unit are constructed using the state switching parameters of the SOFC power generation unit in the state model. SOFC power boundary constraints and power ramping constraints are constructed by using the output power range and power change value of each SOFC power generation unit under normal operating conditions. The standby depth constraint is constructed by the standby depth value and its rate of change of each SOFC power generation unit. Piecewise linearization constraints for SOFC fuel consumption, DC-CDC efficiency, and DCAC efficiency are constructed based on the piecewise linearization results of the SOFC fuel consumption curve, DC-CDC efficiency curve, and DCAC efficiency curve of the SOFC power generation system, respectively. The DCAC grouping constraint condition is constructed by using the input power of the DCAC inverter and the total output power of all the DC-DC converters connected to it.

8. The dispatch control method for SOFC power generation systems according to claim 1, characterized in that, The real-time control parameters corresponding to the decision variables when the mixed-integer optimization model satisfies the decision objective under the given constraints are obtained, including: Solve for the objective decision variables corresponding to the mixed-integer optimization model using the stated decision objective; Based on the target decision variables, obtain one or more real-time control parameters among the target operating status, target output power, target fuel consumption, target thermal standby depth, target DC-DC converter output power, and target DC-AC inverter output power of each SOFC power generation unit; If the output power of each SOFC power generation unit, the DC-DC converter, or the DC-AC inverter under the real-time control parameters does not meet the power boundary conditions corresponding to the constraint conditions, then the real-time control parameters are updated based on the output power until the output power meets the power boundary conditions.

9. A dispatch control device for an SOFC power generation system, characterized in that, The SOFC power generation system includes multiple SOFC power generation units, a DC-DC converter connected to each SOFC power generation unit, and at least one DC-AC inverter; the device includes: State model construction module: used to construct the state model corresponding to each SOFC power generation unit by utilizing the mutual exclusion relationship of the working states of each SOFC power generation unit during the scheduling cycle; Mixed integer optimization model construction module: used to construct a mixed integer optimization model with the operating state, output power, fuel consumption, thermal standby depth, DC-DC output power and DC-AC output power of each SOFC power generation unit in the state model as decision variables, and with the goal of minimizing the total fuel consumption and start-up cost of the SOFC power generation system within the scheduling cycle; Real-time control parameter acquisition module: used to acquire the real-time control parameters corresponding to the decision variables when the mixed integer optimization model satisfies the decision objective under the constraint conditions after constructing the constraint conditions of the mixed integer optimization model based on the target peak-shaving power of the SOFC power generation system; Real-time scheduling and control module: used to schedule the operating status of each SOFC power generation unit, the DC-DC converter and the DC-AC inverter in real time through the real-time control parameters.

10. An electronic device, characterized in that, The electronic device includes a processor and a memory, the memory storing computer-executable instructions that can be executed by the processor, the processor executing the computer-executable instructions to implement the steps of the dispatch control method for an SOFC power generation system according to any one of claims 1 to 8.

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