Day-ahead market optimization operation method and system considering dynamic physical verification

Abstract

The invention discloses a day-ahead market optimization operation method and system considering dynamic physical verification, and relates to the field of energy storage system market operation, and the method comprises the steps: carrying out the multi-objective optimization based on the output characteristics of a fan, taking the wind curtailment rate and the overall operation profit economy as objective functions, setting boundary conditions, and building an upper-layer economic optimization model; based on the physical characteristics of a fan, an internal combustion engine and an energy storage system, a lower-layer physical dynamic model is established, and the lower-layer physical dynamic model is operated for analog simulation; the internal combustion engine participates in quick start and stop, the energy storage system participates in frequency adjustment of small time scale and small power amplitude, and the system is optimal in operation economy in the day-ahead market under the condition of meeting a physical operation boundary through intelligent control and optimal scheduling under continuous verification of a dynamic model; the energy storage system can give full play to the characteristics of high adjusting speed and high adjusting precision, and the internal combustion engine gives play to the advantage of frequent and quick start and stop, so that the benefit mode of the new energy power supply is increased.

Description

Technical Field

[0001] The present invention relates to the field of power generation system scheduling, and in particular to a day-ahead market optimization operation method and system considering dynamic physical verification. Background Art

[0002] In order to promote the green and low-carbon transformation of energy, my country proposed the goal of "carbon peak and carbon neutrality" in 2021. From the industry perspective, current carbon emissions from energy consumption account for about 90% of my country's total emissions. Among them, the power generation industry accounts for the highest proportion, accounting for more than 40% of carbon emissions in the energy industry. Therefore, it is necessary to vigorously develop renewable clean energy such as wind power. In the operation of the power grid, the wind power generation system should achieve controllability of its output power to meet the requirements of the power grid for its dynamic response. This means that the wind power system needs to have the ability to respond quickly and adjust the output power to adapt to changes in power grid demand.

[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, affected by the volatility of wind power, the energy storage system needs to be frequently started and stopped. The frequent start and stop operations of traditional battery energy storage systems will increase the number of battery charge and discharge times, accelerate battery loss, shorten its service life, and are not conducive to the long-term operation of the energy storage system. Internal combustion engine power generation is a relatively mature power generation technology. Internal combustion engine generator sets have the characteristics of fast adjustment speed and high adjustment accuracy. The start and stop time is less than 1 minute, and the frequency modulation rate can reach 6%-100% of the rated output per minute, which is much faster than coal-fired power, gas turbine and other generator sets, and is basically comparable to the electrochemical energy storage regulation performance. 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 solve the problem that frequent start and stop operations of the battery energy storage system will increase the number of battery charge and discharge times, accelerate battery loss, shorten its service life, and be detrimental to the long-term operation of the energy storage 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 optimizing the operation of a day-ahead market considering dynamic physical verification, including:

[0008] Based on the output characteristics of wind turbines, the wind abandonment rate and overall operating profit economy 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, internal combustion engine, and energy storage system, a lower-level physical dynamic model is established, and the lower-level physical dynamic model is run to simulate the working conditions of each power source in actual production operation, and the stability of the combined power generation system is verified;

[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 a combined wind power, internal combustion engine and energy storage operation mode that meets market operation needs, and dynamically adjust the operation plan based on real-time prediction.

[0012] As a preferred solution for the day-ahead market optimization operation method considering dynamic physical verification,

[0013] The multi-objective optimization using the wind curtailment rate and the overall operating profit economy as the objective function includes:

[0014] The first objective function is defined as the day-ahead market revenue of internal combustion engine power generation, the day-ahead market revenue of wind power generation, the day-ahead market revenue of energy storage discharge, the gas fuel cost of the internal combustion engine, the energy storage charging cost, the operation and maintenance cost of wind power, the operation and maintenance cost of the internal combustion engine, and the energy storage operation and maintenance cost. The second objective function is the wind abandonment rate. Set weights and merge the objective functions.

[0015] As a preferred solution for the day-ahead market optimization operation method considering dynamic physical verification,

[0016] The boundary conditions are set as follows:

[0017] Energy storage discharge change rate ≤ maximum energy storage discharge rate

[0018] Energy storage charging power change rate ≤ maximum energy storage charging rate

[0019] Energy storage operating power ≥ minimum energy storage state

[0020] Energy storage operating power ≤ maximum energy storage power

[0021] Wind turbine climbing rate ≤ wind power maximum climbing rate

[0022] Wind turbine output ≤ wind turbine capacity

[0023] Internal combustion engine unit capacity ≤ internal combustion engine unit output ≤ internal combustion engine unit capacity

[0024] The climbing rate of the internal combustion engine is ≤ the maximum climbing rate of the internal combustion engine.

[0025] As a preferred solution for the day-ahead market optimization operation method considering dynamic physical verification,

[0026] The physical characteristics of the wind turbine, the internal combustion engine and the energy storage system are used to establish the lower-level physical dynamic model, which includes:

[0027] The energy storage dynamic model equation is expressed as:

[0028] E soc,t- =E soc,t-1 +O t ΔTηchP ch,t / C BESPS -(1-Ot)ΔTP dis,t / ηdisC BESPS

[0029] As a preferred solution for the day-ahead market optimization operation method considering dynamic physical verification,

[0030] The lower-level physical dynamic model established based on the physical characteristics of the wind turbine, internal combustion engine, and energy storage system also includes: CBESPS is the BESPS capacity; ηch and ηdis are the BESPS charging and discharging efficiencies, respectively; ΔT is the length of the scheduling period, which is generally taken as 15 minutes.

[0031] As a preferred solution for the day-ahead market optimization operation method considering dynamic physical verification,

[0032] The operation of the lower-level physical dynamic model to simulate and emulate the operation stability of the combined power generation system includes:

[0033] The input of the lower-level physical dynamic model is the internal combustion engine power, fuel demand and energy storage; the power and fuel demand correspond one to one; the output is the operation stability verification result, including whether the energy storage operating power and operating temperature are successfully calculated.

[0034] In a second aspect, an embodiment of the present invention provides a joint power generation system scheduling system considering dynamic physical verification, including:

[0035] 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 overall operating profit economy as the objective function, setting boundary conditions, and establishing the upper-level economic optimization model;

[0036] 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, internal combustion engine, and energy storage system, run the lower-level physical dynamic model for simulation, and verify the operating stability of the combined power generation system. The results of the operating stability verification will be fed back to the lower-level physical dynamic model;

[0037] 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;

[0038] The output adjustment module is used to output a wind power, internal combustion engine and energy storage combined output operation mode that meets market operation needs, and dynamically adjust the operation plan based on real-time prediction.

[0039] As a preferred solution for the day-ahead market optimization operation method considering dynamic physical verification,

[0040] Set up the initial operation plan, including the output of wind power, internal combustion engines and energy storage systems;

[0041] According to the initial operation plan, the economic optimization model is used to calculate the day-ahead market profit and wind curtailment rate;

[0042] The operation plan calculated by the upper model is input into the physical dynamic model for stability verification;

[0043] If the physical dynamic model verification passes, the current operation plan is considered feasible; otherwise, the operation plan is adjusted and the upper model calculation and lower model verification are re-performed.

[0044] As a preferred scheme for the day-ahead market optimization operation method considering dynamic physical verification, the optimal operation plan obtained by the iterative optimization algorithm is output, including the output curves of wind power, internal combustion engines and energy storage systems. Based on real-time prediction data, such as wind speed, electricity price, etc., the output of wind power, internal combustion engines and energy storage systems is dynamically adjusted to adapt to market changes.

[0045] The beneficial effects of the present invention are as follows: the internal combustion engine participates in rapid start and stop, and the energy storage system participates in frequency regulation of small time scales and small power amplitudes. Through intelligent control and optimized scheduling, under the continued verification of the dynamic model, the system can operate at the best economic efficiency in the current market while meeting the physical operating boundaries. The energy storage system can give full play to its own characteristics of fast regulation speed and high regulation accuracy, and the internal combustion engine can take advantage of frequent and rapid start and stop, thereby increasing the profit model of new energy power sources. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] 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.

[0047] Figure 1 It is an overall flow chart of the day-ahead market optimization operation method considering dynamic physical verification according to the first embodiment of the present invention;

[0048] Figure 2This is an example diagram of the calculation and operation of the day-ahead market optimization operation method considering dynamic physical verification described in the first embodiment of the present invention. DETAILED DESCRIPTION

[0049] 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.

[0050] 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.

[0051] 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.

[0052] Example 1

[0053] Reference Figure 1 - Figure 2 , which is the first embodiment of the present invention, provides a method for optimizing the operation of the day-ahead market considering dynamic physical verification, including:

[0054] S1: Based on the output characteristics of the wind turbine, the wind abandonment rate and the overall operating profit economy are used as objective functions for multi-objective optimization, boundary conditions are set, and the upper-level economic optimization model is established;

[0055] S2: Based on the physical characteristics of the wind turbine, internal combustion engine, and energy storage system, a lower-level physical dynamic model is established, and the lower-level physical dynamic model is run for simulation, and the working conditions of each power source in actual production operation are simulated to verify the stability of the combined power generation system;

[0056] 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;

[0057] S4: Output a wind power, internal combustion engine and energy storage combined output operation mode that meets market operation needs, and dynamically adjust the operation plan based on real-time prediction.

[0058] In the embodiment of the present application, the multi-objective optimization of using the wind abandonment rate and the overall operating profit economy as objective functions in the above step S1 includes:

[0059] The first objective function is defined as the day-ahead market revenue of internal combustion engine power generation, the day-ahead market revenue of wind power generation, the day-ahead market revenue of energy storage discharge, the gas fuel cost of the internal combustion engine, the energy storage charging cost, the operation and maintenance cost of wind power, the operation and maintenance cost of the internal combustion engine, and the energy storage operation and maintenance cost. The second objective function is the wind abandonment rate. Set weights and merge the objective functions.

[0060] Specifically, the first objective function is: Day-ahead market profit = Day-ahead market revenue of internal combustion engine power generation + Day-ahead market revenue of wind power generation + Day-ahead market revenue of energy storage discharge - gas fuel cost of internal combustion engine - energy storage charging cost - wind power operation and maintenance cost - internal combustion engine operation and maintenance cost - energy storage operation and maintenance cost

[0061] Second objective function: wind curtailment rate (= wind turbine curtailment amount / wind turbine required power generation)

[0062] Weight 1 + Weight 2 = 1

[0063] Merged objective function = weight 1 * first objective function + weight 2 * second objective function

[0064] Among them, weight 1 and weight 2 can be set as needed.

[0065] In the embodiment of the present application, setting the boundary conditions in the above step S1 includes:

[0066] Energy storage discharge change rate ≤ maximum energy storage discharge rate

[0067] Energy storage charging power change rate ≤ maximum energy storage charging rate

[0068] Energy storage operating power ≥ minimum energy storage state

[0069] Energy storage operating power ≤ maximum energy storage power

[0070] Wind turbine climbing rate ≤ wind power maximum climbing rate

[0071] Wind turbine output ≤ wind turbine capacity

[0072] Internal combustion engine unit capacity ≤ internal combustion engine unit output ≤ internal combustion engine unit capacity

[0073] The climbing rate of the internal combustion engine is ≤ the maximum climbing rate of the internal combustion engine.

[0074] In the embodiment of the present application, the establishment of the upper-level economic optimization model in the above step S1 includes:

[0075] Write economic optimization code and run optimization algorithms to find the optimal solution for cost and wind curtailment rate.

[0076] 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:

[0077] Define the objective function and constraints according to the actual situation;

[0078] Set up the initial operation plan and optimization parameters;

[0079] Call minimize function for optimization;

[0080] Output optimization results.

[0081] In the embodiment of the present application, in the above step S2, based on the physical characteristics of the fan, the internal combustion engine, and the internal combustion engine, establishing the lower-level physical dynamic model includes:

[0082] Define the input and output flow equations, expressed as:

[0083] E soc,t =E soc,t-1 +O t ΔTηchP ch,t / C BESPS -(1-Ot)ΔTP dis,t / ηdisC BESPS

[0084] Among them, CBESPS is the capacity of the BESPS energy storage system, that is, the maximum energy that the energy storage system can store.

[0085] ηch is the charging efficiency of the energy storage system, that is, the efficiency of energy conversion during the charging process.

[0086] ηdis is the discharge efficiency of the energy storage system, that is, the efficiency of energy conversion during the discharge process.

[0087] ΔT is the length of the scheduling period, which is generally taken as 15 minutes.

[0088] E soc,t : Represents the state of charge (SOC) of the energy storage system at the end of time step t.

[0089] E soc,t-1 : represents the state of charge of the energy storage system at the beginning of time step t.

[0090] Ot: represents the charging status of the energy storage system within the time step t. If the energy storage system is charging, Ot is 1; if it is not charging, Ot is 0.

[0091] ΔT: represents the time step, that is, the time interval from one time step to the next time step.

[0092] ηch: represents the energy conversion efficiency of the energy storage system during the charging process.

[0093] P ch,t : represents the charging power of the energy storage system within the time step t.

[0094] C BESPS : Indicates the capacity of the energy storage system, that is, the maximum energy that the energy storage system can store.

[0095] (1-Ot): represents the discharge state of the energy storage system within the time step t. If the energy storage system is discharging, (1-Ot) is 1; if it is not discharging, (1-Ot) is 0.

[0096] P dis,t : represents the discharge power of the energy storage system within the time step t.

[0097] ηdis: represents the energy conversion efficiency of the energy storage system during the discharge process.

[0098] Esoc,t-1+OtΔTηchPch,t / CBESPS: indicates that if the energy storage system is charged within time step t, its state of charge will increase, and the amount of increase depends on the charging power, time step, charging efficiency and capacity of the energy storage system.

[0099] -(1-Ot)ΔTPdis,t / ηdisCBESPS: indicates that if the energy storage system is discharged within the time step t, its state of charge will decrease, and the amount of decrease depends on the discharge power, time step, discharge efficiency and capacity of the energy storage system.

[0100] To achieve the best market profit and system stability in a period of time, it is necessary to comprehensively use the upper-level economic optimization model and the lower-level physical dynamic model to perform multiple optimizations:

[0101] Collect historical data over a period of time, including wind speed, load demand, electricity price, etc., and pre-process the data, including data cleaning, missing value processing, outlier processing, etc.

[0102] Based on historical data and system parameters, the upper-level economic optimization model and the lower-level physical dynamic model are initialized, and the initial operation plan is set, including the output plans of wind power, internal combustion engines and energy storage systems.

[0103] For each time step, the upper-level economic optimization model is used to calculate the optimal operation plan for the current time step to minimize the wind curtailment rate and maximize the overall operation profit.

[0104] The upper-level optimization results are used as input to run the lower-level physical dynamic model to simulate the actual operation of the system and verify the stability of the system. According to the feedback from the lower-level model, the parameters and boundary conditions of the upper-level model are adjusted to carry out the next iteration.

[0105] During the iteration process, the operation plan is dynamically adjusted according to real-time forecast data (such as wind speed, load demand and electricity price changes) to ensure that within each time step, the system's operation plan can adapt to market changes and maintain economy and stability.

[0106] The optimization results of each time step are recorded, including the output plan, wind curtailment rate, operating profit, etc. of wind power, internal combustion engines and energy storage systems. The recorded results are analyzed to evaluate the economy and stability of the overall operation plan.

[0107] Based on the iterative optimization results, an operation plan with the best market profit and optimal system stability over a period of time is output. The plan should include detailed information such as output plan, wind abandonment rate, operating profit, and corresponding analysis and explanation.

[0108] The goal of this module is to establish an economic optimization model based on wind turbine output characteristics to minimize the wind curtailment rate and maximize the overall operating profit. The wind curtailment rate refers to the amount of wind power that cannot be used due to grid restrictions or other reasons, while the overall operating profit is the total revenue minus the total cost of wind power, internal combustion engines and energy storage systems. This module will use a multi-objective optimization method to ensure the feasibility of the solution by setting boundary conditions. Boundary conditions may include maximum and minimum output limits for wind power, internal combustion engines and energy storage systems, as well as grid acceptance capacity limits.

[0109] This module will build dynamic models based on the physical characteristics of wind turbines, internal combustion engines and energy storage systems. These models will simulate the actual operation of these systems, including energy conversion efficiency, response time, maintenance requirements and failure modes. By running these models, the operational stability of the combined power generation system can be verified to ensure the stability and reliability of the system under various operating conditions. The verification results will be fed back to the model to adjust the operation strategy and improve the overall performance of the system.

[0110] The iteration module is responsible for iterating the upper and lower models in each time step until the optimal operation scheme is found. This module will record the optimization results of each time step to form an overall optimization scheme. The iteration process may involve multiple adjustments and optimizations to ensure the best balance between economy and stability while meeting all constraints.

[0111] The purpose of the output adjustment module is to output an operation mode of wind power, internal combustion engines and energy storage combined output according to market operation requirements. This module will take into account real-time forecast data such as wind speed, load demand and electricity price changes to dynamically adjust the operation plan. This adjustment is designed to ensure that the system remains economical and stable while meeting market demand. The output adjustment module will ensure that the power generation system can flexibly respond to market changes and improve responsiveness and adaptability.

[0112] 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 optimizing the operation of the day-ahead market considering dynamic physical verification, characterized in that: include: Based on the output characteristics of wind turbines, the wind abandonment rate and overall operating profit economy 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, internal combustion engine, and energy storage system, a lower-level physical dynamic model is established, and the lower-level physical dynamic model is run to simulate the working conditions of each power source in actual production operation, and the stability of the combined power generation system is verified; 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 a combined wind power, internal combustion engine and energy storage operation mode that meets market operation needs, and dynamically adjust the operation plan based on real-time prediction.

2. The method for optimizing the day-ahead market operation considering dynamic physical verification as claimed in claim 1, characterized in that: The multi-objective optimization using the wind curtailment rate and the overall operating profit economy as the objective function includes: The first objective function is defined as the day-ahead market revenue of internal combustion engine power generation, the day-ahead market revenue of wind power generation, the day-ahead market revenue of energy storage discharge, the gas fuel cost of the internal combustion engine, the energy storage charging cost, the operation and maintenance cost of wind power, the operation and maintenance cost of the internal combustion engine, and the energy storage operation and maintenance cost. The second objective function is the wind abandonment rate. Set weights and merge the objective functions.

3. The method for optimizing the day-ahead market operation considering dynamic physical verification as claimed in 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 market profit and wind curtailment rate.

4. The method for optimizing the day-ahead market operation considering dynamic physical verification as claimed in claim 3, characterized in that: The boundary conditions are set as follows: Energy storage discharge change rate ≤ maximum energy storage discharge rate Energy storage charging power change rate ≤ maximum energy storage charging rate Energy storage operating power ≥ minimum energy storage state Energy storage operating power ≤ maximum energy storage power Wind turbine climbing rate ≤ wind power maximum climbing rate 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.

5. The method for optimizing the day-ahead market operation considering dynamic physical verification as claimed in claim 4, characterized in that: The physical characteristics of the wind turbine, the internal combustion engine and the energy storage system are used to establish the lower-level physical dynamic model, which includes: The energy storage dynamic model equation is expressed as: E soc,t =E soc,t-1 +O t ΔTηchP ch,t / C BESPS -(1-Ot)ΔTP dis,t / ηdisC BESPS 。 6. The method for optimizing the day-ahead market operation considering dynamic physical verification as claimed in claim 5, characterized in that: The lower-level physical dynamic model established based on the physical characteristics of the wind turbine, internal combustion engine, and energy storage system also includes: CBESPS is the BESPS capacity; ηch and ηdis are the BESPS charging and discharging efficiencies, respectively; ΔT is the length of the scheduling period, which is generally taken as 15 minutes.

7. The method for optimizing the day-ahead market operation considering dynamic physical verification as claimed in 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, fuel demand and energy storage; the power and fuel demand correspond one to one; the output is the operation stability verification result, including whether the energy storage operating power and operating temperature are successfully calculated.

8. A system using the day-ahead market optimization operation method considering dynamic physical verification as described 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 overall operating profit economy 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, internal combustion engine, and energy storage system, run the lower-level physical dynamic model for simulation, and verify the operating stability of the combined power generation system. The results of the operating stability verification will be fed back 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 a wind power, internal combustion engine and energy storage combined output operation mode that meets market operation needs, and dynamically adjust the operation plan based on real-time prediction.

9. A system using the day-ahead market optimization operation method considering dynamic physical verification as described in any one of claims 1 to 7, characterized in that: include: Set up the initial operation plan, including the output of wind power, internal combustion engines and energy storage systems; According to the initial operation plan, the economic optimization model is used to calculate the day-ahead market profit and wind curtailment rate; The operation plan calculated by the upper model is input into the physical dynamic model for stability verification; If the physical dynamic model verification passes, the current operation plan is considered feasible; Otherwise, adjust the operation plan and recalculate the upper model and verify the lower model. Continuously iterate and optimize until an operating plan is found that meets the physical operating boundary conditions and has the highest current market profit and the lowest wind curtailment rate.

10. A system using the day-ahead market optimization operation method considering dynamic physical verification as described in any one of claims 1 to 7, characterized in that: include: The optimal operation plan obtained by the iterative optimization algorithm is output, including the output curves of wind power, internal combustion engines and energy storage systems. Based on real-time forecast data, wind speed and electricity price, the output of wind power, internal combustion engines and energy storage systems is dynamically adjusted to adapt to market changes.

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