Combined power generation system scheduling method and system considering dynamic physical verification

Abstract

The invention discloses a combined power generation system scheduling method and system considering dynamic physical verification, and relates to the field of power generation system scheduling, and the method comprises the steps: building an upper-layer economic optimization model through multi-objective optimization in combination with the output characteristics of a wind turbine generator and the operation cost of an internal combustion engine, and building a lower-layer economic optimization model based on the physical characteristics of a fan and the internal combustion engine; and establishing a lower-layer dynamic model. Performing iterative optimization on the upper-layer model and the lower-layer model at each time step through analog simulation and operation stability verification to form an overall optimization scheme, outputting a smooth joint output curve, and dynamically adjusting an operation scheme; the advantages of high adjusting speed and high precision of the internal combustion engine are fully utilized, fan operation is more effectively matched, impact on an electric power system is reduced, safe and stable operation of the system is guaranteed, and wide application of renewable energy sources and stable operation of a power grid are promoted under the scene that long-time continuous stable output is needed.

Description

Technical Field

[0001] The present invention relates to the field of power generation system scheduling, and in particular to a method and system for scheduling a combined power generation system considering dynamic physical verification. Background Art

[0002] In recent years, renewable energy has become the main direction of energy transformation. Vigorously developing renewable energy such as wind power, photovoltaics, and hydropower, and realizing a reasonable transition from traditional energy to clean energy is the general trend of energy development and utilization in China and even the world. Wind power generation has significant intermittent and volatile characteristics. The demand for peak and frequency regulation of the power system continues to increase. The power system needs to respond to the power fluctuations of wind power with a larger amplitude, faster speed and higher accuracy to ensure the power balance of the power system and realize the large-scale utilization of renewable energy. As the proportion of wind power generation continues to increase rapidly, the existing frequency-regulated power supply will not be able to meet the peak and frequency regulation needs of the system, and the power system is in urgent need of adding new peak and frequency-regulated power supplies.

[0003] In conventional systems, energy storage systems are often considered to adapt wind power to solve the fluctuation problem caused by wind power access to the grid. Energy storage systems can smooth the intermittent output of renewable energy and reduce energy waste caused by ineffective utilization. By storing and releasing energy, energy storage systems can provide electricity when energy supply is insufficient or demand peaks, thereby improving energy utilization efficiency. However, energy storage systems are affected by their own capacity limitations and may not be able to meet the system's peak power generation needs due to insufficient remaining capacity. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: how to more effectively solve the problem of wind power volatility and improve the stability and economy of the power system.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a method for scheduling a joint power generation system considering dynamic physical verification, including:

[0008] Based on the output characteristics of wind turbines, the wind abandonment rate and the operating cost of internal combustion engines are used as objective functions for multi-objective optimization, boundary conditions are set, and an upper-level economic optimization model is established;

[0009] Based on the physical characteristics of the wind turbine and the internal combustion engine, a lower-level physical dynamic model is established, the lower-level physical dynamic model is run for simulation, and the operation stability of the combined power generation system is verified, and the results of the operation stability verification are fed back to the lower-level physical dynamic model;

[0010] Iterate the upper and lower models to the optimal level at each time step, and record the results of multiple time steps to form an overall optimization solution;

[0011] Output optimized smooth combined output curve and dynamically adjust the operation plan based on real-time prediction.

[0012] As a preferred solution of the combined power generation system dispatching method considering dynamic physical verification, wherein:

[0013] The multi-objective optimization using the wind abandonment rate and the internal combustion engine operating cost as the objective function includes:

[0014] The first objective function is defined as the comprehensive operation and maintenance cost of internal combustion engines and wind power and the gas fuel cost, and the second objective function is the wind abandonment rate. The weights are set and the objective functions are merged.

[0015] As a preferred solution of the combined power generation system dispatching method considering dynamic physical verification, wherein:

[0016] The establishment of the upper-level economic optimization model comprises:

[0017] Write economic optimization code and run optimization algorithms to find the optimal solution for cost and wind curtailment rate.

[0018] As a preferred solution of the combined power generation system dispatching method considering dynamic physical verification, wherein:

[0019] The boundary conditions are set as follows:

[0020] The climbing rate of wind turbines is ≤ the maximum climbing rate of wind power;

[0021] Wind turbine output ≤ wind turbine capacity;

[0022] Internal combustion engine unit capacity ≤ internal combustion engine unit output ≤ internal combustion engine unit capacity;

[0023] The climbing rate of the internal combustion engine is ≤ the maximum climbing rate of the internal combustion engine;

[0024] Internal combustion engine operating temperature ≤ internal combustion engine operating temperature.

[0025] As a preferred solution of the combined power generation system dispatching method considering dynamic physical verification, wherein:

[0026] The establishment of the lower-level physical dynamic model based on the physical characteristics of the fan and the internal combustion engine includes:

[0027] Define the input and output flow equations, expressed as:

[0028] m g +m a +m f =0

[0029] Among them, m g Indicates the gas input flow rate change rate, m a Indicates the rate of change of air inflow, m f Indicates the rate of change of flue gas outflow.

[0030] As a preferred solution of the combined power generation system dispatching method considering dynamic physical verification, wherein:

[0031] The establishment of the lower-level physical dynamic model based on the physical characteristics of the fan and the internal combustion engine also includes:

[0032] Define the heat balance equation, expressed as:

[0033] Q a +Q fc +Q fr -T w -T e =0

[0034] Among them, Q a Indicates the air heat change rate, Q fc Indicates the rate of change of heat carried by fuel, Q fr Indicates the rate of change of heat released by fuel chemical energy, T w Indicates the rate of change of water temperature, T e Indicates the rate of change of ambient temperature.

[0035] As a preferred solution of the combined power generation system dispatching method considering dynamic physical verification, wherein:

[0036] The operation of the lower-level physical dynamic model to simulate and emulate the operation stability of the combined power generation system includes:

[0037] The input of the lower-level physical dynamic model is the internal combustion engine power and fuel demand; the power and fuel demand correspond one to one; the output is the operation stability verification result, including whether the internal combustion engine climbing rate and operating temperature are successfully calculated.

[0038] In a second aspect, an embodiment of the present invention provides a joint power generation system scheduling system considering dynamic physical verification, including:

[0039] The upper-level economic optimization model building module is used to perform multi-objective optimization based on the output characteristics of the wind turbine, taking the wind abandonment rate and the internal combustion engine operating cost as the objective function, setting boundary conditions, and establishing the upper-level economic optimization model;

[0040] The lower-level physical dynamic model building module is used to build the lower-level physical dynamic model based on the physical characteristics of the wind turbine and the internal combustion engine, run the lower-level physical dynamic model for simulation, and verify the operation stability of the combined power generation system, and feed back the results of the operation stability verification to the lower-level physical dynamic model;

[0041] Iteration module, used to iterate the upper and lower models to the optimal level at each time step, and record the results of multiple time steps to form an overall optimization plan;

[0042] The output adjustment module is used to output the optimized smooth combined output curve and dynamically adjust the operation plan based on real-time prediction.

[0043] In a third aspect, an embodiment of the present invention provides a computing device, including:

[0044] Memory and processor;

[0045] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the one or more programs are executed by the one or more processors, the one or more processors implement the combined power generation system scheduling method considering dynamic physical verification as described in any embodiment of the present invention.

[0046] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the method for scheduling a joint power generation system taking into account dynamic physical verification.

[0047] Beneficial effects of the present invention: Through the joint operation of the internal combustion engine, the present invention combines the wind turbine set with the gas internal combustion engine, and through intelligent control and optimized scheduling under the continuous verification of the dynamic model, the system can continuously and stably output a relatively smooth output curve, eliminate the volatility of new energy, reduce the impact on the power system, and ensure the safe and stable operation of the power generation system; different from the energy storage system which is limited by the energy storage capacity, the internal combustion engine can give full play to its own characteristics of fast adjustment speed and high adjustment accuracy, and better match the operation of the wind turbine in the scenario where long-term continuous and stable output is required, which is of great significance to promoting the widespread application of renewable energy and the stable operation of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0049] Figure 1It is an overall flow chart of the method for scheduling a joint power generation system considering dynamic physical verification according to the first embodiment of the present invention;

[0050] Figure 2 This is an example diagram of the calculation and operation of the combined power generation system scheduling method considering dynamic physical verification described in the first embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0052] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0053] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0054] Example 1

[0055] Reference Figure 1-Figure 2 , which is the first embodiment of the present invention, and provides a method for scheduling a joint power generation system considering dynamic physical verification, comprising:

[0056] S1: Based on the output characteristics of the wind turbine, the wind abandonment rate and the operating cost of the internal combustion engine are used as the objective function for multi-objective optimization, boundary conditions are set, and the upper-level economic optimization model is established;

[0057] S2: Based on the physical characteristics of the wind turbine and the internal combustion engine, a lower-level physical dynamic model is established, the lower-level physical dynamic model is run for simulation, and the operation stability of the combined power generation system is verified, and the results of the operation stability verification are fed back to the lower-level physical dynamic model;

[0058] S3: Iterate the upper and lower models to the optimal level at each time step, and record the results of multiple time steps to form an overall optimization solution;

[0059] S4: Outputs the optimized smooth combined output curve and dynamically adjusts the operation plan based on real-time prediction.

[0060] In the embodiment of the present application, the multi-objective optimization using the wind abandonment rate and the internal combustion engine operating cost as objective functions in the above step S1 includes:

[0061] The first objective function is defined as the comprehensive operation and maintenance cost of internal combustion engines and wind power and the gas fuel cost, and the second objective function is the wind abandonment rate. The weights are set and the objective functions are merged.

[0062] Specifically, the first objective function is: gas fuel cost of internal combustion engine + operation and maintenance cost of wind power + operation and maintenance cost of internal combustion engine

[0063] Second objective function: wind curtailment rate (= wind turbine curtailment amount / wind turbine required power generation)

[0064] Weight 1 + Weight 2 = 1

[0065] Merged objective function = weight 1 * first objective function + weight 2 * second objective function

[0066] Among them, weight 1 and weight 2 can be set as needed.

[0067] In the embodiment of the present application, setting the boundary conditions in the above step S1 includes:

[0068] The climbing rate of wind turbines is ≤ the maximum climbing rate of wind power;

[0069] Wind turbine output ≤ wind turbine capacity;

[0070] Internal combustion engine unit capacity ≤ internal combustion engine unit output ≤ internal combustion engine unit capacity;

[0071] The climbing rate of the internal combustion engine is ≤ the maximum climbing rate of the internal combustion engine;

[0072] Internal combustion engine operating temperature ≤ internal combustion engine operating temperature.

[0073] In the embodiment of the present application, the establishment of the upper-level economic optimization model in the above step S1 includes:

[0074] Write economic optimization code and run optimization algorithms to find the optimal solution for cost and wind curtailment rate.

[0075] In the embodiment of the present application, Python can be used to write economic optimization code and run the optimization algorithm to find the optimal solution for cost and wind abandonment rate.

[0076] In another possible implementation, MATLAB or Julia can also be used to write economic optimization codes:

[0077] MATLAB: Using MATLAB's Optimization Toolbox or Global Optimization Toolbox, you can implement a variety of optimization algorithms (such as genetic algorithms, particle swarm optimization, simulated annealing, etc.), and combine them with the economic models of internal combustion engines and wind power to perform multi-objective optimization.

[0078] Julia: Julia language is suitable for solving complex optimization problems due to its high performance and powerful scientific computing libraries (such as JuMP, Optim, etc.). You can write Julia code and combine the economic models of internal combustion engines and wind power for multi-objective optimization.

[0079] In the embodiment of the present application, in the above step S2, based on the physical characteristics of the fan and the internal combustion engine, establishing the lower-level physical dynamic model includes:

[0080] Define the input and output flow equations, expressed as:

[0081] m g +m a +m f =0

[0082] Among them, m g Indicates the gas input flow rate change rate, m a Indicates the rate of change of air inflow, m f Indicates the rate of change of flue gas outflow;

[0083] Define the heat balance equation, expressed as:

[0084] Q a +Q fc +Q fr -T w -T e =0

[0085] Among them, Q a Indicates the air heat change rate, Q fc Indicates the rate of change of heat carried by fuel, Q fr Indicates the rate of change of heat released by fuel chemical energy, T w Indicates the rate of change of water temperature, T e Indicates the rate of change of ambient temperature.

[0086] In the embodiment of the present application, the operation of the lower physical dynamic model for simulation in the above step S2 and the verification of the operation stability of the combined power generation system include:

[0087] The input of the lower-level physical dynamic model is the internal combustion engine power and fuel demand; the power and fuel demand correspond one to one; the output is the operation stability verification result, including whether the internal combustion engine climbing rate and operating temperature are successfully calculated.

[0088] In the embodiment of the present application, a dynamic model of a fan and an internal combustion engine can be established based on the dymola / modelica platform;

[0089] In another possible implementation, ANSYS Fluent and ANSYS Workbench can also be used to establish dynamic models of the fan and internal combustion engine, and perform simulation and optimization:

[0090] ANSYS Fluent: Mainly used for fluid dynamics (CFD) simulation, suitable for simulating fluid flow and heat transfer processes in fan blades and internal combustion engine combustion chambers.

[0091] ANSYS Workbench: Provides multi-physics simulation capabilities that can integrate simulation models in multiple fields such as power, thermodynamics, and electricity to ensure the coupling and coordination of various physical processes.

[0092] In another possible implementation, MATLAB / Simulink can also be used to establish dynamic models of the fan and the internal combustion engine, and perform simulation and optimization:

[0093] Simulink: Suitable for system-level simulation and control design. It can build dynamic models of fans and internal combustion engines and implement multi-physics field simulation through the Simscape library.

[0094] Optimization Toolbox: Integrates multiple optimization algorithms (such as genetic algorithms, linear programming, etc.) and can be used for multi-objective optimization design and automatic parameter optimization.

[0095] In another possible implementation, COMSOL Multiphysics can also be used to establish dynamic models of the fan and internal combustion engine, and perform simulation and optimization:

[0096] COMSOL Multiphysics: Provides powerful multi-physics simulation capabilities, suitable for coupled simulation of fans and internal combustion engines, including fluid dynamics, heat transfer, structural mechanics and other fields.

[0097] COMSOL LiveLink for MATLAB: Integrate COMSOL models with MATLAB to facilitate model optimization and development of automatic control strategies.

[0098] In another possible implementation, in the above step S3, the upper and lower layer models are iterated to the optimum at each time step, and the results of multiple time steps are recorded to form an overall optimization scheme. The optimization process can be divided into multiple time steps (for example, by hours or minutes), and the upper and lower layer models are iteratively optimized in each time step. According to historical data and real-time operating status, the division of time steps and the parameter settings of each time step are adjusted in real time to adapt to changes in system requirements.

[0099] In another possible implementation, in the above step S3, iterating the upper and lower models to the optimum at each time step, and recording the results of multiple time steps to form an overall optimization plan can adopt stepwise optimization (such as alternating geometry method) or reinforcement learning and other methods, and continuously adjust and optimize the operation plan through interactive feedback of the upper and lower models. Introduce intelligent optimization algorithms such as genetic algorithms and particle swarm optimization, comprehensively consider multiple objective functions, and ensure that a better solution is achieved in each time step.

[0100] In another possible implementation, in the above step S3, the upper and lower models are iterated to the optimum at each time step, and the results of multiple time steps are recorded to form an overall optimization plan. The operating results of each time step (such as output, cost, wind abandonment rate, temperature, etc.) can also be recorded in detail to facilitate in-depth analysis and evaluation of various performance indicators of the model.

[0101] In another possible implementation, evaluation indicators (such as convergence and stability) may be set to judge the effectiveness of the optimization algorithm and analyze the improvement of each iteration.

[0102] For example, a sensitivity analysis is performed on the model parameters to identify the key parameters that have a greater impact on the optimization results and make targeted adjustments. The robustness of the model is tested under different operating conditions (such as wind speed and load changes) to ensure that the optimization effect is maintained in actual operation.

[0103] In another possible implementation, the optimized smooth combined output curve is output in the above step S4, and the operation plan is dynamically adjusted based on real-time prediction. The combined output curve output at each time step may fluctuate, and smoothing methods such as sliding average and weighted average can be used to eliminate short-term fluctuations and improve operability.

[0104] In another possible implementation, the optimized smooth combined output curve is output in the above step S4, and the operation plan is dynamically adjusted based on real-time prediction. The output results of different time steps can also be linearly interpolated to form a more continuous and smooth output curve for subsequent operation or decision-making.

[0105] The above is a schematic scheme of the combined power generation system scheduling method considering dynamic physical verification in this embodiment. It should be noted that the technical scheme of the combined power generation system scheduling system considering dynamic physical verification and the technical scheme of the combined power generation system scheduling method considering dynamic physical verification belong to the same concept. For details not described in detail in the technical scheme of the combined power generation system scheduling system considering dynamic physical verification in this embodiment, please refer to the description of the technical scheme of the combined power generation system scheduling method considering dynamic physical verification.

[0106] In this embodiment, the combined power generation system dispatching system considering dynamic physical verification includes:

[0107] The upper-level economic optimization model building module is used to perform multi-objective optimization based on the output characteristics of the wind turbine, taking the wind abandonment rate and the internal combustion engine operating cost as the objective function, setting boundary conditions, and establishing the upper-level economic optimization model;

[0108] The lower-level physical dynamic model building module is used to build the lower-level physical dynamic model based on the physical characteristics of the wind turbine and the internal combustion engine, run the lower-level physical dynamic model for simulation, and verify the operation stability of the combined power generation system, and feed back the results of the operation stability verification to the lower-level physical dynamic model;

[0109] Iteration module, used to iterate the upper and lower models to the optimal level at each time step, and record the results of multiple time steps to form an overall optimization plan;

[0110] The output adjustment module is used to output the optimized smooth combined output curve and dynamically adjust the operation plan based on real-time prediction.

[0111] This embodiment further provides a computing device, which is applicable to the case of a combined power generation system scheduling method considering dynamic physical verification, including:

[0112] A memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the combined power generation system scheduling method considering dynamic physical verification as proposed in the above embodiment.

[0113] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for scheduling a joint power generation system taking into account dynamic physical verification as proposed in the above embodiment is implemented.

[0114] The storage medium proposed in this embodiment and the joint power generation system scheduling method considering dynamic physical verification proposed in the above embodiment belong to the same inventive concept. The technical details not described in detail in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0115] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for dispatching a combined power generation system considering dynamic physical verification, characterized in that: include: Based on the output characteristics of wind turbines, the wind abandonment rate and the operating cost of internal combustion engines are used as objective functions for multi-objective optimization, boundary conditions are set, and an upper-level economic optimization model is established; Based on the physical characteristics of the wind turbine and the internal combustion engine, a lower-level physical dynamic model is established, the lower-level physical dynamic model is run for simulation, and the operation stability of the combined power generation system is verified, and the results of the operation stability verification are fed back to the lower-level physical dynamic model; Iterate the upper and lower models to the optimal level at each time step, and record the results of multiple time steps to form an overall optimization solution; Output optimized smooth combined output curve and dynamically adjust the operation plan based on real-time prediction.

2. The method for dispatching a joint power generation system considering dynamic physical verification according to claim 1, characterized in that: The multi-objective optimization using the wind abandonment rate and the internal combustion engine operating cost as the objective function includes: The first objective function is defined as the comprehensive operation and maintenance cost of internal combustion engines and wind power and the gas fuel cost, and the second objective function is the wind abandonment rate. The weights are set and the objective functions are merged.

3. The method for dispatching a joint power generation system considering dynamic physical verification according to claim 2, characterized in that: The establishment of the upper-level economic optimization model comprises: Write economic optimization code and run optimization algorithms to find the optimal solution for cost and wind curtailment rate.

4. The method for dispatching a joint power generation system considering dynamic physical verification according to claim 3, characterized in that: The boundary conditions are set as follows: The climbing rate of wind turbines is ≤ the maximum climbing rate of wind power; Wind turbine output ≤ wind turbine capacity; Internal combustion engine unit capacity ≤ internal combustion engine unit output ≤ internal combustion engine unit capacity; The climbing rate of the internal combustion engine is ≤ the maximum climbing rate of the internal combustion engine; Internal combustion engine operating temperature ≤ internal combustion engine operating temperature.

5. The method for dispatching a joint power generation system considering dynamic physical verification according to claim 4, characterized in that: The establishment of the lower-level physical dynamic model based on the physical characteristics of the fan and the internal combustion engine includes: Define the input and output flow equations, expressed as: m g +m a +m f =0 Among them, m g Indicates the gas input flow rate change rate, m a Indicates the air inflow rate change rate, m f Indicates the rate of change of flue gas outflow.

6. The method for dispatching a joint power generation system considering dynamic physical verification according to claim 5, characterized in that: The establishment of the lower-level physical dynamic model based on the physical characteristics of the fan and the internal combustion engine also includes: Define the heat balance equation, expressed as: Q a +Q fc +Q fr -T w -T e =0 Among them, Q a Indicates the air heat change rate, Q fc Indicates the rate of change of heat carried by fuel, Q fr Indicates the rate of change of heat released by fuel chemical energy, T w Indicates the rate of change of water temperature, T e Indicates the rate of change of ambient temperature.

7. The method for dispatching a combined power generation system considering dynamic physical verification according to claim 6, characterized in that: The operation of the lower-level physical dynamic model to simulate and emulate the operation stability of the combined power generation system includes: The input of the lower-level physical dynamic model is the internal combustion engine power and fuel demand; the power and fuel demand correspond one to one; the output is the operation stability verification result, including whether the internal combustion engine climbing rate and operating temperature are successfully calculated.

8. A system using the method for dispatching a combined power generation system taking into account dynamic physical verification as claimed in any one of claims 1 to 7, characterized in that: include: The upper-level economic optimization model building module is used to perform multi-objective optimization based on the output characteristics of the wind turbine, taking the wind abandonment rate and the internal combustion engine operating cost as the objective function, setting boundary conditions, and establishing the upper-level economic optimization model; The lower-level physical dynamic model building module is used to build the lower-level physical dynamic model based on the physical characteristics of the wind turbine and the internal combustion engine, run the lower-level physical dynamic model for simulation, and verify the operation stability of the combined power generation system, and feed back the results of the operation stability verification to the lower-level physical dynamic model; Iteration module, used to iterate the upper and lower models to the optimal level at each time step, and record the results of multiple time steps to form an overall optimization plan; The output adjustment module is used to output the optimized smooth combined output curve and dynamically adjust the operation plan based on real-time prediction.

9. A computing device comprising: Memory and processor; The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the combined power generation system scheduling method considering dynamic physical verification as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the method for scheduling a combined power generation system considering dynamic physical verification as described in any one of claims 1 to 7.

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