Offshore wind power energy storage system fine modeling and scheduling method

Abstract

The application provides a fine modeling and scheduling method for offshore wind power energy storage systems, and belongs to the technical field of new energy power system scheduling and electricity-to-alcohol coupling optimization. The technical problems of insufficient modeling accuracy of offshore wind power electricity-to-alcohol cogeneration systems, insufficient representation of equipment operation characteristics, and difficult simultaneous optimization of economy and new energy consumption in the scheduling process are solved. The technical scheme is as follows: S1, preprocessing wind power output based on annual wind data of a wind farm; S2, constructing a two-stage fine decoupling model of electricity-to-alcohol; S3, constructing a solution storage mode carbon capture device model; S4, reducing the total cost of the power transmission network under high penetration of new energy as the optimization target. The application can finely represent the dynamic adjustment characteristics of the electricity-to-alcohol system under offshore wind power fluctuations, improve the wind power consumption capacity, and reduce the system operation cost.

Description

Technical Field

[0001] This invention relates to the field of new energy power system dispatching and power-to-energy coupling optimization technology, and in particular to a refined modeling and dispatching method for offshore wind power energy storage systems. Background Technology

[0002] With the large-scale development of offshore wind power, the installed capacity of offshore wind farms has grown rapidly. In the scenario of grid connection of offshore wind power clusters, the system often faces the coexistence of limited wind power absorption and wind curtailment due to factors such as random fluctuations in offshore wind resources, limited transmission capacity, and grid power flow constraints. To improve the absorption of renewable energy and realize the cross-time conversion and cross-carrier utilization of electricity, the technology of producing green chemicals from electricity (PtX) has become an important path. Among them, PtM ​​(Pt-to-methanol) can convert fluctuating green electricity into storable and transportable green fuels or chemical feedstocks, and has good potential for system peak shaving and absorption.

[0003] Existing research on the optimized operation of electro-to-methanol systems considers the steady-state model of the entire production process from the perspective of the electro-methanol coupling production mechanism. This model treats the electrolytic hydrogen production process and the methanol synthesis process as a whole, performing equivalent modeling and unified scheduling. However, this simple modeling method struggles to characterize the differences in operating characteristics between the electrolyzer and the methanol synthesis equipment. Consequently, in scenarios where both sea breeze fluctuations and grid power constraints exist, scheduling strategies may become overly conservative or deviate from the actual operating patterns of the equipment, affecting the system's economic efficiency and absorption capacity. Therefore, research on the adjustment flexibility of the electro-to-methanol process under sea breeze conditions remains relatively lacking.

[0004] How to solve the above-mentioned technical problems is the challenge facing this invention. Summary of the Invention

[0005] The purpose of this invention is to provide a refined modeling and scheduling method for offshore wind power energy storage systems. This method is based on annual wind power data from wind farms, utilizes the K-means clustering algorithm to identify typical daily power generation scenarios, constructs a refined decoupled two-stage electro-to-methanol conversion model and introduces a condition switching mechanism, uses mixed-integer linear programming to reveal the impact of sea wind fluctuations on the dynamic adjustment characteristics of electro-to-methanol operation, calculates the optimal operating states of the electrolyzer and methanol synthesis equipment, and proposes a refined modeling and scheduling method for offshore wind power energy storage systems with the optimization objective of reducing the total cost of the power grid under high new energy penetration.

[0006] The inventive concept of this invention is as follows: The method includes: preprocessing wind power output based on annual wind power data from wind farms, and using the K-means clustering algorithm to obtain typical daily power generation scenarios; constructing a refined decoupled model for two stages of electro-to-methanol conversion, establishing equipment power-energy consumption-output relationships in the electrolysis hydrogen production stage and the methanol synthesis stage respectively, and introducing a working condition switching mechanism to realize constraints on the start-up, ramp-up, and standby or shutdown operation states of the methanol synthesis equipment; constructing a carbon capture device model using solution storage, and determining the carbon capture device operation strategy based on the supply and demand material balance relationship of the methanol synthesis equipment; and establishing and solving a mixed integer linear programming model including power transmission constraints with the optimization objective of reducing the total cost of the power grid when the penetration rate of new energy is 50%, obtaining the optimal operating states of wind power grid connection and curtailment decisions, electrolyzers, and methanol synthesis equipment, revealing the impact of sea wind fluctuations on the dynamic regulation characteristics of electro-to-methanol conversion, and outputting a scheduling scheme. This invention can accurately characterize the dynamic regulation characteristics of the electro-to-methanol system under offshore wind power fluctuations, improve wind power absorption capacity, and reduce system operating costs.

[0007] To better achieve the above-mentioned objectives, this invention is implemented through the following technical solution: a refined modeling and scheduling method for an offshore wind power-to-methanol cogeneration system, comprising the following steps:

[0008] S1. Based on the annual wind power data of the wind farm, the wind power output is preprocessed, and the K-means clustering algorithm is used to obtain several daily power generation scenarios to characterize the characteristics of sea wind fluctuations.

[0009] S2. Construct a refined decoupled model for the two-stage electro-to-methanol conversion. Establish the equipment power-energy consumption-output relationship in the electrolysis hydrogen production stage and the methanol synthesis stage respectively. Introduce a working condition switching mechanism to realize the constraints of the methanol synthesis equipment's start-up, ramp-up, standby or shutdown operation status.

[0010] S3. Construct a carbon capture device model using solution storage, and solve the operation strategy of the carbon capture device based on the supply and demand material balance relationship of the methanol synthesis equipment.

[0011] S4. With the goal of reducing the penetration rate of new energy to 50% and the total cost of the power grid, a mixed integer linear programming model with power transmission constraints is established and solved to obtain the optimal operating state of wind power grid connection and wind curtailment decisions, electrolyzers and methanol synthesis equipment, reveal the impact of sea breeze fluctuations on the dynamic regulation characteristics of electro-methanol conversion and output the scheduling scheme.

[0012] Preferably, step S1 includes:

[0013] S1-1. Determine the number of clusters k and the cluster centroids. Calculate the distance between N samples and k cluster centroids. Assign the N samples to the cluster centroids with the smallest distance values, forming k clusters. The distance from each sample to the centroid is calculated using the following formula:

[0014]

[0015] In the formula: The distance between the i-th sample and the j-th cluster center; Let i be the value of the i-th sample on the k-th feature; The value of the j-th cluster center on the k-th feature; The total number of features;

[0016] S1-2. Calculate the average value of all objects in each class, and use the calculated average value as the new cluster centers for the k clusters. Determine whether the clusters have converged using the following formula:

[0017]

[0018] In the formula: This represents the clustering error function value. Let be the set of samples in the i-th cluster, representing all samples that have been assigned to the i-th cluster; Let i be the cluster center of the i-th class; for The sample of class q in the class.

[0019] S1-3. The output power of a wind turbine is closely related to its rated power and wind speed. The output power of a wind turbine is calculated using the following formula:

[0020]

[0021] In the formula: It is the output power of the wind power cluster. This is the rated power of the wind power cluster; It is the real-time wind speed of the wind power cluster; , and These are the rated wind speed, cut-in wind speed, and cut-off wind speed of the wind power cluster.

[0022] Preferably, step S2 includes:

[0023] S2-1. The working process of the electrolytic cell is divided into shutdown, startup, hot standby, and running states. Considering the equipment's heating process, the cold start process has a relatively long and generally non-negligible start-up delay. Including the electrolytic cell's operating condition switching and start-up / shutdown time constraints, it can be expressed as:

[0024]

[0025] In the formula, The variable is a 0-1 variable representing the shutdown state of the electrolytic cell at time t; The variable is a 0-1 variable characterizing the start-up state of the electrolyzer at time t; The variable is a 0-1 variable characterizing the hot standby state of the electrolytic cell at time t; A 0-1 variable characterizing the operating state of the electrolyzer at time t; For the shutdown state of the electrolytic cell at time t-1, there are 0-1 variables; For the electrolytic cell startup state at time t-1, there are 0-1 variables. for A 0-1 variable representing the current state of the electrolytic cell during shutdown; for The 0-1 variable representing the hot standby status of the electrolytic cell at any given moment; for A 0-1 variable representing the starting state of the electrolytic cell at any given time; The index variable is used for summation; t is the time variable. This represents the shortest shutdown time for the electrolytic cell; This is the longest standby time for the electrolytic cell; This refers to the start-up time of the electrolytic cell;

[0026] 2-2. The electrical power consumed in the electrolysis process mainly includes the electrical power consumed during startup, the standby power, and the operating power. Therefore, the electrical power of the electrolytic cell is expressed by the following formula:

[0027]

[0028] In the formula, Let t be the electrical power consumed by the electrolytic cell at time t; Let be the electrical power consumed by the electrolytic cell at time t-1; The electrical power consumed when the electrolytic cell is turned on; The fixed insulation power for the electrolytic cell; Let t be the hydrogen production power of the electrolyzer at time t; The power consumption required to produce one unit of hydrogen in an electrolyzer; Let t be the amount of hydrogen produced by the electrolyzer at time t; , These are the upper and lower limits of hydrogen production capacity of the electrolyzer, respectively. The upward slope rate of the electrolytic cell; The downward slope rate of the electrolytic cell; This refers to the rated power of the electrolytic cell;

[0029] 2-3. The working process of a methanol synthesis unit includes three switching actions: cold start, hot start, and shutdown. In actual operation, the switching time for some states is on the order of hours. The start-up and shutdown process and time constraints of the methanol synthesis unit can be expressed by the following formula:

[0030]

[0031] In the formula, A 0-1 variable characterizing the shutdown state of the methanol synthesis equipment at time t; This is a 0-1 variable characterizing the heating state of the methanol synthesis equipment bed at time t; This is a 0-1 variable characterizing the feedstock input status of the methanol synthesis equipment at time t; This is a 0-1 variable characterizing the thermal standby status of the methanol synthesis equipment at time t; A 0-1 variable characterizing the operating state of the methanol synthesis equipment at time t; A 0-1 variable characterizing the shutdown state of the methanol synthesis equipment at time t-1; This is a 0-1 variable characterizing the heating state of the methanol synthesis equipment at time t-1; This is a 0-1 variable characterizing the feedstock input status of the methanol synthesis equipment at time t-1; To characterize A 0-1 variable representing the shutdown status of the methanol synthesis equipment at any given moment; To characterize A 0-1 variable representing the heating state of the warm bed in the methanol synthesis equipment at any given moment; To characterize A 0-1 variable representing the feedstock input status of the methanol synthesis equipment at any given time; For summation index variables; This represents the shortest shutdown time for the methanol synthesis equipment. Heating time for methanol synthesis equipment; For the time required to put the methanol synthesis equipment into operation;

[0032] 2-4. The electrical power consumed during the operation of the methanol synthesis equipment consists of four parts: the power consumed during startup, the standby power during short-term shutdown, the operating power during normal operation, and the power consumed by the feed gas before switching to operation. This can be expressed by the following formula:

[0033]

[0034] In the formula, in the formula, Let be the electrical power consumed by the methanol synthesis equipment at time t; This represents the electrical power consumed by the methanol synthesis unit at time t-1. The electrical power consumed for heating the warm bed in the methanol synthesis equipment; The electrical power consumed by the methanol synthesis equipment for material input; The fixed insulation power for the methanol synthesis equipment; The production power of the methanol synthesis equipment at time t; The power consumption for methanol synthesis equipment to produce methanol; The amount of methanol produced by the methanol synthesis equipment at time t; This represents the upper limit of the methanol production capacity of methanol synthesis equipment; This represents the lower limit of the methanol production capacity of methanol synthesis equipment; The upward ramp rate of the methanol synthesis equipment; The downhill ramp rate of the methanol synthesis plant; MSS is an abbreviation for methanol synthesis plant. Rated power of the methanol synthesis equipment; power consumption. Represented as:

[0035]

[0036] In the formula, The electrical power of the reaction tower; for The amount of substance; for The amount of substance; R is the compressor efficiency; T is the gas constant; and T is the gas temperature. for Pressure before compression; for The pressure after compression; for Pressure before compression; for The pressure after compression; This is the hydrogen consumption coefficient; This is the carbon dioxide consumption coefficient;

[0037] Preferably, step S3 includes:

[0038] S3-1. Establish a carbon capture device model for solution storage using a rich-lean liquid tank. The working model is as follows:

[0039]

[0040] In the formula, The power consumption of the carbon capture device at time t; Let t be the basic power consumption of the carbon capture device at time t; The operating power consumption of the carbon capture device at time t; The power consumption for capturing each unit of carbon dioxide in a carbon capture device; Captured at time t ; , These are the upper and lower limits of the power consumption of the carbon capture device, respectively; among which... Calculations are performed using the regeneration tower, absorption tower, and rich solution. The expression is:

[0041]

[0042] In the formula, For the regeneration tower quantity; For the absorption tower quantity; For the rich liquid quantity; For the efficiency of carbon capture devices The flue gas split ratio of the absorption tower; The absorption efficiency of the absorption tower; The coal-fired power plant was captured at time t. ; For delivery to methanol synthesis equipment quantity; For carbon sequestration quantity;

[0043] S3-2, Combine the volume of the rich liquid with the content of... The following formula is used to establish a balance between them:

[0044]

[0045] In the formula, In the rich liquid tank The solution concentration; Let be the volume released from the rich liquid tank at time t; Let be the volume released from the lean liquid tank at time t; Let be the liquid volume stored in the rich liquid tank at time t; Let be the liquid volume stored in the rich liquid tank at time t-1; Let be the volume of liquid stored in the lean liquid tank at time t; Let be the liquid volume stored in the lean liquid tank at time t-1; This represents the upper limit of liquid storage in the rich liquid tank; This is the upper limit of the liquid storage capacity of the lean liquid tank; The initial liquid volume in the rich liquid tank; The final liquid storage volume for the rich liquid tank; The initial liquid volume in the lean liquid tank; To schedule the final liquid level in the lean liquid tank; To optimize the scheduling time period;

[0046] Preferably, step S4 includes:

[0047] S4-1. Establish an optimization scheduling model with multiple constraints, aiming to minimize total cost. The optimization objective is:

[0048]

[0049] In the formula, f is the total cost of the system; The cost of electricity generation from thermal power plants; The maintenance cost of the equipment in the system; For carbon trading costs;

[0050] S4-2. Introduce the supply and demand material balance relationship of the methanol synthesis equipment, satisfying the following equation:

[0051]

[0052] In the formula, Let t be the amount of hydrogen required for the methanol synthesis equipment at time t; Let t be the amount of carbon dioxide that the methanol synthesis equipment needs to consume at time t; The amount of methanol produced by the methanol synthesis equipment at time t; This is the hydrogen consumption coefficient; This is the carbon dioxide consumption coefficient;

[0053] S4-3. Introducing power flow constraints, expressed mathematically as follows:

[0054]

[0055] In the formula, For line power flow; These represent the minimum and maximum allowable transmission capacities for line power flow.

[0056] S4-4. Build a simulation model corresponding to the optimized scheduling in the simulation software Matlab, verify the flexible dynamic adjustment capability of refined modeling of electricity-to-methanol conversion under wind power fluctuations, and solve the optimal scheduling strategy of the system through YALMIMP analysis.

[0057] Meanwhile, this invention proposes a refined modeling and scheduling system for offshore wind power energy storage systems. The system, applying the method described in this invention, includes the following steps:

[0058] The wind power modeling module is configured to perform the following process: preprocessing wind power output based on annual wind data of wind farms, and using the K-means clustering algorithm to obtain typical daily power generation scenarios to characterize the characteristics of sea wind fluctuations.

[0059] The electro-to-methanol decoupling module is configured to perform the following process: construct a two-stage fine decoupling model for electro-to-methanol conversion, establish equipment power-energy consumption-output relationships in the electrolysis hydrogen production stage and the methanol synthesis stage respectively, and introduce a working condition switching mechanism to realize equipment start-up, ramp-up and standby or shutdown operation state constraints.

[0060] The solution storage carbon capture device model building module is configured to execute the following process: based on the supply and demand material balance of the methanol synthesis plant, solve for the operating strategy of the carbon capture device;

[0061] The optimization model building module is configured to execute the following process: Taking the reduction of the total transmission network cost when the penetration rate of new energy sources reaches 50% as the optimization objective, a mixed-integer linear programming model including power transmission constraints is established and solved to obtain the optimal operating states of wind power grid connection and curtailment decisions, electrolyzers, and methanol synthesis equipment. The impact of sea breeze fluctuations on the dynamic regulation characteristics of electro-methanol conversion is revealed, and a scheduling scheme is output. Simultaneously, this invention proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed, it implements the steps of the method described in this invention.

[0062] Furthermore, the present invention proposes a computer-readable storage medium having a computer program stored thereon, the computer program being configured to implement the steps of the method described in the present invention when invoked by a processor.

[0063] Finally, the present invention provides a computer program product comprising a computer program / instructions that, when executed by a processor, implement the steps of the method described in the present invention.

[0064] Compared with the prior art, the present invention has the following technical effects:

[0065] (1) Based on wind farm wind data, this invention uses the K-means clustering algorithm to obtain several daily power generation scenarios to characterize the fluctuation characteristics of sea wind. Then, the scenarios are embedded into the grid connection and power-to-methanol optimization model to realize the power-to-methanol production process in the large-scale application scenario of sea wind, laying the scenario foundation for the application of the two-stage refined operation strategy and system optimization scheduling of power-to-methanol.

[0066] (2) The method of the present invention can characterize the differentiated operation mechanism of electrolysis and synthesis equipment, decouple the hydrogen production stage and methanol synthesis stage by modeling, and introduce a working condition switching mechanism. The start-up, ramp-up and standby and shutdown operation characteristics of the equipment are characterized by state variables and state transition constraints, so that the hydrogen production and synthesis equipment can achieve independent and coordinated dynamic adjustment according to their respective power-energy consumption-yield relationship, thereby more accurately revealing the impact of sea breeze fluctuations on the electro-methanol operation strategy.

[0067] (3) This invention proposes an electric-hydrogen-methanol coordinated scheduling strategy for grid connection of new energy with high penetration rate. It integrates the "constraint set of offshore wind power cluster grid connection and transmission network operation" and the "constraint set of two-stage equipment for electric-to-methanol conversion" into a mixed integer linear programming model. The optimization objective is to reduce the total cost of the transmission network under high new energy penetration rate. The optimal operating state sequence of wind power grid connection, wind curtailment decision and electrolyzer and methanol synthesis equipment is obtained, and a system decision scheme that can be directly used for scheduling is output. Based on the optimization results, under the premise of meeting the load priority and network security constraints, the electric-to-methanol unit can be guided to "full power consumption" during windy periods and "give way to ensure supply" during peak load periods, in response to the policy of advocating local consumption of new energy. Attached Figure Description

[0068] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0069] Figure 1 This invention relates to a flowchart of a refined modeling and scheduling method for offshore wind power energy storage systems.

[0070] Figure 2 This is a schematic diagram of the electrolytic cell in operation according to the present invention.

[0071] Figure 3 This is a schematic diagram of the start-up and shutdown process of the methanol synthesis equipment of the present invention.

[0072] Figure 4 This invention uses a traditional model of electricity-to-ethanol conversion to output a power system dispatch diagram under conditions of limited power transmission.

[0073] Figure 5 This invention provides a diagram showing the output of methanol production and equipment operation status using a traditional electro-methanol conversion model under conditions of limited power transmission.

[0074] Figure 6 This invention uses a two-stage refined model of electricity-to-ethanol conversion to output a power system dispatching diagram under the condition of limited power transmission.

[0075] Figure 7 This invention provides a refined two-stage electrolysis-to-ethanol conversion model to output an electrolyzer operating status diagram under conditions of limited power transmission.

[0076] Figure 8 This invention provides a refined two-stage electro-methanol conversion model to output methanol production and equipment operation status diagrams under conditions of limited power transmission.

[0077] Figure 9 This invention outputs a power system dispatch diagram using a traditional model of electricity-to-ethanol conversion under conditions where power transmission is unrestricted.

[0078] Figure 10 This invention outputs methanol production and equipment operation status diagrams using a traditional electro-methanol conversion model under conditions where power transmission is unrestricted.

[0079] Figure 11 This invention outputs a power system dispatching diagram using a two-stage refined model of electricity-to-ethanol conversion under conditions where power transmission is unrestricted.

[0080] Figure 12 This invention provides an electrolyzer operating status diagram using a refined two-stage electro-to-ethanol conversion model under conditions where power transmission is unrestricted.

[0081] Figure 13 This invention provides a refined two-stage electro-methanol conversion model that outputs methanol production and equipment operation status diagrams under conditions where power transmission is unrestricted.

[0082] The parameters in the attached diagram are defined as follows:

[0083] in, Figure 4 , Figure 9 In this context, WT represents the grid-connected power of the offshore wind power cluster; load represents the local load; MSS represents the power consumption of the power-to-methanol equipment under traditional modeling; G represents the power generation of the thermal power plant; delivery represents the power transmitted to other regions; and CCUS represents the power consumption of the carbon capture device.

[0084] in, Figure 6 , Figure 11 In this context, WT represents the grid-connected power of the offshore wind power cluster; load represents the local load; EL+SD represents the power consumption of the electrolyzer and seawater desalination unit; MSS represents the power consumption of the methanol synthesis unit; G represents the power generation of the thermal power plant; delivery represents the power transmitted to other regions; and CCUS represents the power consumption of the carbon capture unit.

[0085] in, Figure 7 , Figure 12 In this context, EL represents the output of the electrolyzer; HES represents the hydrogen filling and discharging of the hydrogen storage tank; and MSS represents the amount of hydrogen consumed by the methanol synthesis equipment. Detailed Implementation

[0086] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. Of course, the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0087] Example 1: This example demonstrates the application of a refined modeling and scheduling method for offshore wind power energy storage systems in a simulation model containing a two-stage refined model of electricity-to-ethanol conversion. The method's flowchart is as follows. Figure 1 The specific steps are as follows:

[0088] (1) Analysis of wind farm output on a typical day:

[0089] When annual wind power data from wind farms is available, the K-means clustering algorithm is used to transform complex and redundant time-series data into a representative and typical scenario. The number of clusters k and the cluster centers are determined. The distance values ​​between N samples and k cluster centers are calculated. The N samples are then assigned to the cluster centers with the smallest distance values, forming k clusters. The distance from each sample to the center of the cluster is then obtained.

[0090]

[0091] In the formula: Let be the distance between the i-th sample and the j-th cluster center; Let i be the value of the i-th sample on the k-th feature; The value of the j-th cluster center on the k-th feature; The total number of features;

[0092] Calculate the average value of all objects in each class, and use the calculated average value as the new cluster centers for the k clusters. Determine whether the clusters have converged using the following formula:

[0093]

[0094] In the formula: This represents the clustering error function value. Let be the set of samples in the i-th cluster, representing all samples that have been assigned to the i-th cluster; Let i be the cluster center of the i-th class; for The qth sample in the class; repeat the above steps until E reaches the limiting condition or the fluctuation is very small, indicating that the algorithm tends to be stable at this time, and the intraday wind force data under typical daytime conditions are obtained, and the clustering ends.

[0095] When the turbine models and parameters of the units within a wind farm are identical, assuming that all units have the same output power, a wind turbine model is used to simulate the power generation of the entire wind farm. The output power of a wind turbine is closely related to its rated power and wind speed, thus yielding a model of the wind turbine's output power:

[0096]

[0097] In the formula: It is the output power of the wind power cluster. This is the rated power of the wind power cluster; It is the real-time wind speed of the wind power cluster; , and These are the rated wind speed, cut-in wind speed, and cut-off wind speed of the wind power cluster.

[0098] (2) Switching between two-stage operation modes of electro-to-methanol conversion:

[0099] Figure 2 This diagram illustrates the operation of an electrolytic cell. The cell's operating states are divided into shutdown, startup, hot standby, and operation. Considering the equipment's heating process, the cold start process has a relatively long and generally non-negligible start-up delay. The electrolytic cell can only transition from shutdown to hot standby or startup operation when the minimum start-up time is met. Since the duration of hot start-up and shutdown processes is short, no additional constraints are imposed. Therefore, the operating condition switching and start-up / shutdown time constraints can be expressed as:

[0100]

[0101] in The variable is a 0-1 variable representing the shutdown state of the electrolytic cell at time t; The variable is a 0-1 variable characterizing the start-up state of the electrolyzer at time t; The variable is a 0-1 variable characterizing the hot standby state of the electrolytic cell at time t; A 0-1 variable characterizing the operating state of the electrolyzer at time t; For the shutdown state of the electrolytic cell at time t-1, there are 0-1 variables; For the electrolytic cell startup state at time t-1, there are 0-1 variables. for A 0-1 variable representing the current state of the electrolytic cell during shutdown; for The 0-1 variable representing the hot standby status of the electrolytic cell at any given moment; for A 0-1 variable representing the starting state of the electrolytic cell at any given time; The index variable is used for summation; t is the time variable. This represents the shortest shutdown time for the electrolytic cell; This is the longest standby time for the electrolytic cell; This refers to the start-up time of the electrolytic cell; the electrical power consumed in the electrolysis process mainly includes the electrical power consumed during startup, reserve power, and operating power. When the electrolytic cell starts up, the temperature of the cell will not reach the required level for production for a period of time. At this temperature condition, the consumed electrical power is used to generate heat to raise the temperature of the electrolytic cell. As the temperature of the electrolytic cell continues to rise until it can generate... When the electrolytic cell is shut down, the power is the insulation power of the electrolytic cell; however, when the electrolytic cell is shut down, the power can be instantly reduced to 0, and the power consumed during shutdown need not be considered. The electrolysis process follows Faraday's law, which states that the amount of substance undergoing chemical change at the electrode interface is directly proportional to the amount of electricity passed through it, thus producing… The quantity and power consumption can be approximated as a linear relationship. The power consumed in the electrolysis process includes the power consumed during startup, the reserve power, and the operating power. The power of the electrolytic cell is expressed by the following formula:

[0102]

[0103] In the formula, Let t be the electrical power consumed by the electrolytic cell at time t; Let be the electrical power consumed by the electrolytic cell at time t-1; The electrical power consumed when the electrolytic cell is turned on; The fixed insulation power for the electrolytic cell; Let t be the hydrogen production power of the electrolyzer at time t; The power consumption required to produce one unit of hydrogen in an electrolyzer; Let t be the amount of hydrogen produced by the electrolyzer at time t; , These are the upper and lower limits of hydrogen production capacity of the electrolyzer, respectively. The upward slope rate of the electrolytic cell; The downward slope rate of the electrolytic cell; This refers to the rated power of the electrolytic cell;

[0104] Figure 3 This diagram illustrates the start-up and shutdown process of a methanol synthesis unit. The operation of the unit includes several switching processes: cold start, hot start, and shutdown. In actual operation, the switching time for some states is on the order of hours. The methanol synthesis reaction typically takes place under high temperature and high pressure. The cold start process includes bed heating and feedstock input; the hot start process only includes feedstock input. To avoid drastic power fluctuations that could reduce equipment lifespan, the unit must transition from the operating state to a standby state to reduce pressure before entering the shutdown state. During the standby state, the unit is depressurized and kept warm. Within a certain time range, the bed temperature for methanolization can still reach the temperature required for catalyst activity, allowing for rapid hot start-up. However, if this time range is exceeded, the unit will enter a shutdown state. The start-up and shutdown process and time constraints of the methanol synthesis unit are expressed by the following formula:

[0105]

[0106] In the formula, A 0-1 variable characterizing the shutdown state of the methanol synthesis equipment at time t; This is a 0-1 variable characterizing the heating state of the methanol synthesis equipment bed at time t; This is a 0-1 variable characterizing the feedstock input status of the methanol synthesis equipment at time t; This is a 0-1 variable characterizing the thermal standby status of the methanol synthesis equipment at time t; A 0-1 variable characterizing the operating state of the methanol synthesis equipment at time t; A 0-1 variable characterizing the shutdown state of the methanol synthesis equipment at time t-1; This is a 0-1 variable characterizing the heating state of the methanol synthesis equipment at time t-1; This is a 0-1 variable characterizing the feedstock input status of the methanol synthesis equipment at time t-1; To characterize A 0-1 variable representing the shutdown status of the methanol synthesis equipment at any given moment; To characterize A 0-1 variable representing the heating state of the warm bed in the methanol synthesis equipment at any given moment; To characterize A 0-1 variable representing the feedstock input status of the methanol synthesis equipment at any given time; For summation index variables; This represents the shortest shutdown time for the methanol synthesis equipment. Heating time for methanol synthesis equipment; For the time required to put the methanol synthesis equipment into operation;

[0107] The electrical power consumed during the operation of the methanol synthesis unit consists of four parts: the power consumed during startup, the standby power during short-term shutdown, the operating power during normal operation, and the power consumed by the feed gas before switching to operation. This can be expressed by the following formula:

[0108]

[0109] In the formula, Let be the electrical power consumed by the methanol synthesis equipment at time t; This represents the electrical power consumed by the methanol synthesis unit at time t-1. The electrical power consumed for heating the warm bed in the methanol synthesis equipment; The electrical power consumed by the methanol synthesis equipment for material input; The fixed insulation power for the methanol synthesis equipment; The production power of the methanol synthesis equipment at time t; The power consumption for methanol synthesis equipment to produce methanol; The amount of methanol produced by the methanol synthesis equipment at time t; This represents the upper limit of the methanol production capacity of methanol synthesis equipment; This represents the lower limit of the methanol production capacity of methanol synthesis equipment; The upward ramp rate of the methanol synthesis equipment; The downhill ramp rate of the methanol synthesis plant; MSS is an abbreviation for methanol synthesis plant. This refers to the rated power of the methanol synthesis equipment.

[0110] power consumption Represented as:

[0111]

[0112] In the formula, The electrical power of the reaction tower; for The amount of substance; for The amount of substance; R is the compressor efficiency; T is the gas constant; and T is the gas temperature. for Pressure before compression; for The pressure after compression; for Pressure before compression; for The pressure after compression; This is the hydrogen consumption coefficient; This is the carbon dioxide consumption coefficient.

[0113] (3) Carbon capture system model with solution storage constrained by hydrocarbon flow equation:

[0114] if Directly captured by carbon capture devices, the power consumption of carbon capture will be comparable to... The capture rate exhibits a highly consistent linear relationship, which will increase the burden on thermal power units during peak load periods. Introducing rich and lean liquid tanks can effectively... The absorption and capture processes are separated, and the liquid in the rich liquid tank can be extracted through the regeneration tower. The treated liquid flows into the lean solution tank for the next absorption. Preparations are underway. A model of a carbon capture, utilization, and storage facility incorporating rich and lean liquid tanks is established, and its working model is as follows:

[0115]

[0116] In the formula, The power consumption of the carbon capture device at time t; Let t be the basic power consumption of the carbon capture device at time t; The operating power consumption of the carbon capture device at time t; The power consumption for capturing each unit of carbon dioxide in a carbon capture device; Captured at time t ; , These represent the upper and lower limits of the power consumption of the carbon capture device; the carbon capture device's... Analyze the capture source, all captured... All of these originate from the regeneration tower and are transported to the methanol synthesis unit to generate methanol, ultimately achieving carbon recycling. The regenerated carbon in the regeneration tower... Absorbed from the absorption tower And the discharge from the rich liquid tank The absorption tower absorbs Emissions from thermal power units;

[0117] The expression is:

[0118]

[0119] In the formula, For the regeneration tower quantity; For the absorption tower quantity; For the rich liquid quantity; For the efficiency of carbon capture devices The flue gas split ratio of the absorption tower; The absorption efficiency of the absorption tower; The coal-fired power plant was captured at time t. ; For delivery to methanol synthesis equipment quantity; For carbon sequestration quantity;

[0120] In liquid and containing There is also a linear relationship between the volume of the liquid and the volume of the solution, which is related to the volume of the solution. Concentration-dependent, the volume of the rich liquid is related to the content The following formula is used to establish a balance between them:

[0121]

[0122] In the formula, In the rich liquid tank The solution concentration; Let be the volume released from the rich liquid tank at time t; Let be the volume released from the lean liquid tank at time t; Let be the liquid volume stored in the rich liquid tank at time t; Let be the liquid volume stored in the rich liquid tank at time t-1; Let be the volume of liquid stored in the lean liquid tank at time t; Let be the liquid volume stored in the lean liquid tank at time t-1; This represents the upper limit of liquid storage in the rich liquid tank; This is the upper limit of the liquid storage capacity of the lean liquid tank; The initial liquid volume in the rich liquid tank; The final liquid storage volume for the rich liquid tank; The initial liquid volume in the lean liquid tank; To schedule the final liquid level in the lean liquid tank; To optimize the scheduling time period;

[0123] (4) A two-stage refined energy system optimization scheduling model for electro-ethanol conversion:

[0124] This paper explores how to determine the operating strategy of an electro-ethanol conversion equipment by solving a mixed-integer linear programming problem. The aim is to study the adjustment flexibility of the electro-ethanol conversion process under conditions that closely approximate the actual operating characteristics of the equipment, and to improve the system's economic efficiency. An optimization scheduling model with multiple constraints is established, with the objective of minimizing total cost. The optimization objective is as follows:

[0125] ;

[0126] In the formula, f is the total cost of the system; The cost of electricity generation from thermal power plants; The maintenance cost of the equipment in the system; For carbon trading costs;

[0127] According to the methanol synthesis equation, the reactants and products satisfy a mass ratio relationship. The electrolyzer, carbon capture unit, and methanol synthesis equipment should also satisfy the following relationship: Introducing the material balance relationship of the methanol synthesis equipment, the following equations must be satisfied:

[0128]

[0129] In the formula, Let t be the amount of hydrogen required for the methanol synthesis equipment at time t; Let t be the amount of carbon dioxide that the methanol synthesis equipment needs to consume at time t; The amount of methanol produced by the methanol synthesis equipment at time t; This is the hydrogen consumption coefficient; This is the carbon dioxide consumption coefficient;

[0130] Introducing power flow constraints, the formula is expressed as follows:

[0131]

[0132] In the formula, For line power flow; These represent the minimum and maximum allowable transmission capacities for line power flow.

[0133] A simulation model corresponding to the optimized scheduling was built in the Matlab simulation software laboratory to verify the flexible dynamic adjustment capability of the refined modeling of the power-to-ethanol conversion under wind power fluctuations. The optimal scheduling strategy of the system was analyzed and solved using the Yalmip toolkit.

[0134] Table 1 shows the economic efficiency and renewable energy consumption of this embodiment. Scheme 1 uses the traditional power-to-methanol model under power transmission constraints; Scheme 2 uses a refined two-stage power-to-methanol model under power transmission constraints; Scheme 3 uses the traditional power-to-methanol model under unrestricted power transmission; and Scheme 4 uses a refined two-stage power-to-methanol model under unrestricted power transmission. The results in Table 1 show that compared with the traditional model, the total system cost, wind curtailment volume, and wind curtailment rate are all reduced after adopting the refined two-stage power-to-methanol model. Among them, Scheme 4 has the lowest total cost of 3,191,118 yuan, and the wind curtailment volume and wind curtailment rate are 18,219.39 MW and 7.61%, respectively. This indicates that the method of the present invention can effectively reduce the system operating cost and improve the offshore wind power consumption capacity under different power transmission conditions.

[0135] Table 1 Comparison of economic efficiency and renewable energy consumption in this embodiment

[0136]

[0137] First, a refined two-stage electro-ethanol conversion model is set up under a scenario where power transmission is limited. For comparison, a traditional equivalent model for electro-ethanol conversion is also used. Figure 4 and Figure 6 The power dispatch results show that, under conditions of limited power transmission, while the traditional electro-methanol equivalent model can achieve a certain degree of wind power absorption by equating the methanol system to a polymerization load, it fails to adequately characterize the dynamic coupling relationship within the electrolysis-hydrogen production-methanol synthesis-carbon utilization chain, making it difficult to fully unleash the flexibility of the production process. In contrast, the refined two-stage electro-methanol model transforms the system from a "polymerization load" to a "multi-stage coordinated regulation," prioritizing the rapid absorption of fluctuating power by the front-end electrolysis unit, while the back-end methanol synthesis and carbon utilization stages complete cross-time-period coordinated conversion within a more reasonable operating range, significantly improving the energy and material matching relationship within the system. The results indicate that this model reduces wind curtailment from 22841.81 MW to 20222.28 MW, the curtailment rate from 9.54% to 8.45%, and simultaneously reduces the total system cost by 24.98%. Among them, the cost of methanol synthesis and carbon trading decreased significantly, indicating that the refined model significantly improved the operational economy and physical rationality of the electro-methanol system by reconstructing the temporal coordination relationship on the process side.

[0138] Furthermore, Figure 5 , Figure 7 , Figure 8This study reveals the regulatory flexibility of a refined electro-to-methanol model. Under conditions of limited power line transmission, the traditional equivalent model, which treats the electrolysis hydrogen production and methanol synthesis processes as agglomerates, exhibits strong synchronous coupling characteristics in methanol production. Although the system can maintain high output during periods of wind power surplus, it is prone to prolonged shutdowns in the later stages, reflecting its high dependence on instantaneous power conditions. In contrast, the two-stage refined electro-to-methanol model explicitly distinguishes the operation processes of the electrolysis and synthesis sides, enabling the electrolyzer to act as a flexible and independent front-end responding to grid fluctuations, while the methanol synthesis equipment resumes operation at more suitable times. The results show that under the refined model, the electrolysis side maintains continuous regulation capability in the middle stage, and the methanol synthesis process is transformed from a "complete shutdown" in the traditional model to a multi-stage structure of "intermittent recovery—re-operation at high load." The two-stage refined model effectively releases the temporal decoupling potential within the electro-to-methanol chain, realizing a collaborative scheduling mechanism of "front-end wave absorption and back-end stabilization," thereby significantly enhancing the operational continuity, process flexibility, and engineering feasibility of the methanol system under limited power grid scenarios.

[0139] Furthermore, verification was conducted in scenarios not subject to power transmission limitations. Figure 9 , Figure 11 Analyzing the scheduling results, compared to traditional equivalent modeling, the refined two-stage electro-to-methanol model more fully reflects the differences in operating mechanisms and adjustment flexibility between different models, enabling a more reasonable coordination between the rapid response capability of the front-end electrolysis stage and the stable operating conditions required by the back-end methanol synthesis stage. It not only avoids simply treating the electro-to-methanol process as a single response object, but also highlights the heterogeneity of the front and rear processes in terms of dynamic characteristics, operating boundaries, and adjustment methods. Overall, the refined modeling can more accurately characterize the internal process characteristics and synergistic relationships of electro-to-methanol, enabling the system to have stronger flexible adjustment capabilities and process adaptability under complex operating scenarios, thus providing stronger support for improving the utilization level of new energy, improving system operating quality, and optimizing scheduling paths.

[0140] Depend on Figure 10 , Figure 12 , Figure 13 The comparison results show that, without the upper limit on power transmission, the methanol production under the electro-to-methanol equivalent model is more dependent on the prevailing power conditions, and electrolysis and methanol synthesis exhibit strong synchronous response characteristics. Therefore, when wind power fluctuates or system balance pressure increases, methanol production is more likely to experience a significant decline or even a temporary interruption. The refined model does not merely change the distribution of the production curve, but enables the system to cope with external fluctuations through the division of labor between upstream and downstream stages and hierarchical adjustments within the process. This scheduling characteristic is closer to the operating logic of electrolysis and synthesis units in actual industrial systems, and also indicates that refined modeling can improve the system's adaptability to fluctuating wind power.

[0141] Example 2: Based on the mathematical model, simulations were performed using a model built in the Matrix Lab simulation software. An electro-ethanol conversion operation strategy was set up under conditions of limited power transmission; for comparison, an electro-ethanol conversion operation strategy without power transmission limitations was also set up. Figure 6 , Figure 11 The power dispatch results, under the premise of adopting the two-stage refined model of power-to-methanol conversion, show that changes in power transmission conditions significantly affect the system dispatch strategy. In transmission-constrained scenarios, the system tends to achieve local surplus wind power consumption through the coordinated operation of electrolyzers, methanol synthesis, and carbon utilization, with the power-to-methanol system primarily serving as a buffer for local congestion and a regional self-balancing function. In transmission-unconstrained scenarios, the power grid can initially share some of the balancing tasks, and the power-to-methanol system operates more within an economic range that matches the power generation situation of the transmission grid, demonstrating the dispatch characteristics of process optimization and coordination. The results show that compared to Scheme 2, Scheme 4 reduces the total cost from 3,395,439 yuan to 3,191,118 yuan, the wind curtailment from 20,222.28 MW to 18,219.39 MW, and the wind curtailment rate from 8.45% to 7.61%. This indicates that the two-stage refined model can dynamically adjust the operation mode of upstream and downstream equipment according to changes in network boundary conditions: strengthening local consumption capacity when the network is constrained, and improving overall operational economy when the network is relaxed, thus demonstrating strong system adaptability and optimization value.

[0142] according to Figure 7 , Figure 8 , Figure 12 , Figure 13 Comparing the electrolyzer output and methanol output under both power transmission-constrained and unconstrained scenarios reveals that the refined two-stage electro-to-methanol model can dynamically adjust the scheduling strategies of the preceding and following stages based on changes in network boundary conditions. In the transmission-constrained scenario, the electrolyzer undertakes a stronger function of absorbing local surplus power, while the methanol synthesis unit focuses on phased recovery and timely operation, further absorbing surplus wind power. This demonstrates that this modeling approach can alleviate transmission bottlenecks and enhance regional self-balancing capabilities. In the unconstrained scenario, the transmission network can initially share some of the balancing pressure, allowing the electrolyzer and methanol synthesis unit to operate collaboratively within a more optimal operating range. This results in smoother output transitions and stronger process continuity, with its main role shifting towards improving overall system efficiency and process economy. This indicates that the refined two-stage model not only enhances the adaptability of the electro-to-methanol system to fluctuating wind power but also enables a strategy shift from local absorption-oriented to global optimization-oriented approaches under different network conditions, ultimately achieving a scheduling strategy that balances electrolysis flexibility, synthesis stability, and energy flow synergy.

[0143] Example 3: This example proposes a refined modeling and scheduling system for offshore wind power energy storage systems, applying the steps of the method described in this invention. The system includes:

[0144] The wind power modeling module is configured to perform the following process: preprocessing wind power output based on annual wind data of wind farms, and using the K-means clustering algorithm to obtain typical daily power generation scenarios to characterize the characteristics of sea wind fluctuations.

[0145] The electro-to-methanol decoupling module is configured to perform the following process: construct a two-stage fine decoupling model for electro-to-methanol conversion, establish equipment power-energy consumption-output relationships in the electrolysis hydrogen production stage and the methanol synthesis stage respectively, and introduce a working condition switching mechanism to realize equipment start-up, ramp-up and standby or shutdown operation state constraints.

[0146] The solution storage carbon capture device model building module is configured to execute the following process: based on the supply and demand material balance of the methanol synthesis plant, solve for the operating strategy of the carbon capture device;

[0147] The optimization model building module is configured to execute the following process: Taking the reduction of the total transmission network cost when the penetration rate of new energy reaches 50% as the optimization objective, a mixed-integer linear programming model including power transmission constraints is established and solved to obtain the optimal operating states of wind power grid connection and curtailment decisions, electrolyzers, and methanol synthesis equipment. The impact of sea breeze fluctuations on the dynamic regulation characteristics of electro-methanol conversion is revealed, and a scheduling scheme is output. Example 4: This example proposes an electronic system, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the method steps of the present invention.

[0148] Example 5: This example proposes a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the steps of the method described in this invention, which will not be repeated here.

[0149] Example 6: This example proposes a computer program product, including a computer program / instructions. When the computer program / instructions are executed by a processor, they implement the steps of the method described in this invention, which will not be repeated here.

[0150] It should be noted that the processing flow of embodiments 3-6 corresponds to the specific steps of the method provided in embodiment 1 of the present invention, and has the corresponding functional modules and beneficial effects of the method. Technical details not described in detail in this embodiment can be found in the method provided in embodiment 1 of the present invention.

[0151] The program code used to implement the methods of this application may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0152] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A refined modeling and scheduling method for offshore wind power energy storage systems, characterized in that: Includes the following steps: S1. Based on the annual wind power data of the wind farm, the wind power output is preprocessed, and the K-means clustering algorithm is used to obtain several daily power generation scenarios to characterize the characteristics of sea wind fluctuations. S2. Construct a refined decoupled model for the two-stage electro-to-methanol conversion. Establish the equipment power-energy consumption-output relationship in the electrolysis hydrogen production stage and the methanol synthesis stage respectively. Introduce a working condition switching mechanism to realize the constraints of the methanol synthesis equipment's start-up, ramp-up, standby or shutdown operation status. S3. Construct a carbon capture device model using solution storage, and solve the operation strategy of the carbon capture device based on the supply and demand material balance relationship of the methanol synthesis equipment. S4. With the goal of reducing the penetration rate of new energy to 50% and the total cost of the power grid, a mixed integer linear programming model with power transmission constraints is established and solved to obtain the optimal operating state of wind power grid connection and wind curtailment decisions, electrolyzers and methanol synthesis equipment, reveal the impact of sea breeze fluctuations on the dynamic regulation characteristics of electro-methanol conversion and output the scheduling scheme.

2. The refined modeling and scheduling method for offshore wind power energy storage systems according to claim 1, characterized in that, S1 includes the following steps: S1-1. Determine the number of clusters k and the cluster centroids. Calculate the distance between N samples and k cluster centroids. Assign the N samples to the cluster centroids with the smallest distance values, forming k clusters. Obtain the centroid distance of each sample from the endpoint using the following formula: ； In the formula: Let be the distance between the i-th sample and the j-th cluster center. Let i be the value of the i-th sample on the k-th feature; The value of the j-th cluster center on the k-th feature; The total number of features; S1-2. Calculate the average value of all objects in each cluster, and use the calculated average value as the new cluster centers for the k clusters. Determine whether the clusters have converged using the following formula: ； In the formula: This represents the clustering error function value. Let be the set of samples in the i-th cluster, representing all samples that have been assigned to the i-th cluster; Let i be the cluster center of the i-th class; for The q-th sample in the class; S1-3. The output power of a wind turbine is related to its rated power and wind speed. The output power of a wind turbine is calculated using the following formula: ； In the formula: It is the output power of the wind power cluster. This is the rated power of the wind power cluster; It is the real-time wind speed of the wind power cluster; , and These are the rated wind speed, cut-in wind speed, and cut-off wind speed of the wind power cluster.

3. The refined modeling and scheduling method for offshore wind power energy storage systems according to claim 1, characterized in that, S2 includes the following steps: S2-1. The electrolytic cell's operating process is divided into shutdown, startup, hot standby, and running states. Due to the equipment's heating process, the cold start-up time extension, including the electrolytic cell's operating condition switching and start-up / shutdown time constraints, is expressed as follows: ； In the formula, Let be a 0-1 variable representing the shutdown state of the electrolytic cell at time t; Let be a 0-1 variable representing the start-up state of the electrolytic cell at time t; Let be a 0-1 variable representing the hot standby state of the electrolytic cell at time t; Let t be a 0-1 variable representing the operating state of the electrolytic cell at time t; For the shutdown state of the electrolytic cell at time t-1, there are 0-1 variables; Let be a 0-1 variable representing the shutdown state of the electrolytic cell at time t; For the electrolytic cell startup state at time t-1, there are 0-1 variables. for A 0-1 variable representing the current state of the electrolytic cell during shutdown; for The 0-1 variable representing the hot standby status of the electrolytic cell at any given moment; for A 0-1 variable representing the starting state of the electrolytic cell at any given time; The index variable is used for summation; t is the time variable. This represents the shortest shutdown time for the electrolytic cell; This is the longest standby time for the electrolytic cell; This refers to the start-up time of the electrolytic cell; 2-2. The electrical power consumed in the electrolysis process includes the electrical power consumed during startup, the standby power, and the operating power. The electrical power of the electrolytic cell is expressed by the following formula: ； In the formula, Let t be the electrical power consumed by the electrolytic cell at time t; Let be the electrical power consumed by the electrolytic cell at time t-1; The electrical power consumed when the electrolytic cell is turned on; The fixed insulation power for the electrolytic cell; Let t be the hydrogen production power of the electrolyzer at time t; The power consumption required to produce one unit of hydrogen in an electrolyzer; Let t be the amount of hydrogen produced by the electrolyzer at time t; , These are the upper and lower limits of hydrogen production capacity of the electrolyzer, respectively. The upward slope rate of the electrolytic cell; The downward slope rate of the electrolytic cell; This refers to the rated power of the electrolytic cell; 2-3. The working process of the methanol synthesis equipment includes three switching actions: cold start, hot start, and shutdown. In actual operation, the switching time for some states is on the order of hours. The start-up and shutdown process and time constraints of the methanol synthesis equipment are expressed by the following formula: ； In the formula, A 0-1 variable characterizing the shutdown state of the methanol synthesis equipment at time t; This is a 0-1 variable characterizing the heating state of the methanol synthesis equipment bed at time t; This is a 0-1 variable characterizing the feedstock input status of the methanol synthesis equipment at time t; This is a 0-1 variable characterizing the thermal standby status of the methanol synthesis equipment at time t; A 0-1 variable characterizing the operating state of the methanol synthesis equipment at time t; A 0-1 variable characterizing the shutdown state of the methanol synthesis equipment at time t-1; This is a 0-1 variable characterizing the heating state of the methanol synthesis equipment at time t-1; This is a 0-1 variable characterizing the feedstock input status of the methanol synthesis equipment at time t-1; To characterize A 0-1 variable representing the shutdown status of the methanol synthesis equipment at any given moment; To characterize A 0-1 variable representing the heating state of the warm bed in the methanol synthesis equipment at any given moment; To characterize A 0-1 variable representing the feedstock input status of the methanol synthesis equipment at any given time; For summation index variables; This represents the shortest shutdown time for the methanol synthesis equipment. Heating time for methanol synthesis equipment; For the time required to put the methanol synthesis equipment into operation; 2-4. The electrical power consumed during the operation of the methanol synthesis equipment consists of four parts: the power consumed during startup, the standby power during short-term shutdown, the operating power during normal operation, and the power consumed by the feed gas before switching to operation. This can be expressed by the following formula: ； In the formula, Let be the electrical power consumed by the methanol synthesis equipment at time t; This represents the electrical power consumed by the methanol synthesis unit at time t-1. The electrical power consumed for heating the warm bed in the methanol synthesis equipment; The electrical power consumed by the methanol synthesis equipment for material input; The fixed insulation power for the methanol synthesis equipment; The production power of the methanol synthesis equipment at time t; The power consumption for methanol synthesis equipment to produce methanol; The amount of methanol produced by the methanol synthesis equipment at time t; This represents the upper limit of the methanol production capacity of methanol synthesis equipment; This represents the lower limit of the methanol production capacity of methanol synthesis equipment; The upward ramp rate of the methanol synthesis equipment; The downhill ramp rate of the methanol synthesis plant; MSS is an abbreviation for methanol synthesis plant. This refers to the rated power of the methanol synthesis equipment. power consumption Represented as: ； In the formula, The electrical power of the reaction tower; for The amount of substance; for The amount of substance; R is the compressor efficiency; T is the gas constant; and T is the gas temperature. for Pressure before compression; for The pressure after compression; for Pressure before compression; for The pressure after compression; This is the hydrogen consumption coefficient; This is the carbon dioxide consumption coefficient.

4. The refined modeling and scheduling method for offshore wind power energy storage systems according to claim 1, characterized in that, S3 includes the following steps: S3-1. Establish a carbon capture device model for solution storage using a rich-lean liquid tank. The working model is as follows: ； In the formula, The power consumption of the carbon capture device at time t; Let t be the basic power consumption of the carbon capture device at time t; The operating power consumption of the carbon capture device at time t; The power consumption for capturing each unit of carbon dioxide in a carbon capture device; Captured at time t ; , These are the upper and lower limits of the power consumption of the carbon capture device, respectively; among which... Calculations are performed using the regeneration tower, absorption tower, and rich solution. The expression is: ； In the formula, For the regeneration tower quantity; For the absorption tower quantity; For the rich liquid quantity; For the efficiency of carbon capture devices The flue gas split ratio of the absorption tower; The absorption efficiency of the absorption tower; The coal-fired power plant was captured at time t. ; For delivery to methanol synthesis equipment quantity; For carbon sequestration quantity; S3-2, Combine the volume of the rich liquid with the content of... The following formula is used to establish a balance between them: ； In the formula, In the rich liquid tank The solution concentration; Let be the volume released from the rich liquid tank at time t; Let be the volume released from the lean liquid tank at time t; Let be the liquid volume stored in the rich liquid tank at time t; Let be the liquid volume stored in the rich liquid tank at time t-1; Let be the volume of liquid stored in the lean liquid tank at time t; Let be the liquid volume stored in the lean liquid tank at time t-1; This represents the upper limit of liquid storage in the rich liquid tank; This is the upper limit of the liquid storage capacity of the lean liquid tank; The initial liquid volume in the rich liquid tank; The final liquid storage volume for the rich liquid tank; The initial liquid volume in the lean liquid tank; To schedule the final liquid level in the lean liquid tank; To optimize the scheduling time period.

5. The refined modeling and scheduling method for offshore wind power energy storage systems according to claim 1, characterized in that, S4 includes the following steps: S4-1. Establish an optimization scheduling model with the objective of minimizing total cost and including multiple constraints. The optimization objective is: ； In the formula, f is the total cost of the system; The cost of electricity generation from thermal power plants; The maintenance cost of the equipment in the system; For carbon trading costs; S4-2. Introduce the supply and demand material balance relationship of the methanol synthesis equipment, satisfying the following equation: ； In the formula, Let t be the amount of hydrogen consumed by the methanol synthesis equipment at time t; Let t be the amount of carbon dioxide consumed by the methanol synthesis equipment at time t; The amount of methanol produced by the methanol synthesis equipment at time t; This is the hydrogen consumption coefficient; This is the carbon dioxide consumption coefficient; S4-3. Introducing power flow constraints, the formula is expressed as follows: ； In the formula, For line power flow; , These are the minimum and maximum allowable transmission capacities for line power flow, respectively. S4-4. Build and optimize the simulation model corresponding to the scheduling in the Matlab simulation software matrix laboratory to verify the flexible dynamic adjustment capability of the refined modeling of the power-to-ethanol conversion under wind power fluctuations. Analyze and solve the optimal scheduling strategy of the system using the Yalmip toolkit.

6. A refined modeling and scheduling system for offshore wind power energy storage systems, characterized in that, The system comprising the steps of applying the method according to any one of claims 1 to 5, wherein the system includes: The wind power modeling module is configured to perform the following process: preprocessing wind power output based on annual wind data of wind farms, and using the K-means clustering algorithm to obtain typical daily power generation scenarios to characterize the characteristics of sea wind fluctuations. The electro-to-methanol decoupling module is configured to perform the following process: construct a two-stage fine decoupling model for electro-to-methanol conversion, establish equipment power-energy consumption-output relationships in the electrolysis hydrogen production stage and the methanol synthesis stage respectively, and introduce a working condition switching mechanism to realize equipment start-up, ramp-up and standby or shutdown operation state constraints. The solution storage carbon capture device model building module is configured to perform the following process: based on the supply and demand material balance of the methanol synthesis equipment, solve the operation strategy of the carbon capture device; The optimization model building module is configured to perform the following process: with the optimization objective of reducing the total cost of the power grid when the penetration rate of new energy is 50%, a mixed integer linear programming model with power transmission constraints is established and solved to obtain the optimal operating state of wind power grid connection and wind curtailment decisions, electrolyzers and methanol synthesis equipment, reveal the impact of sea breeze fluctuations on the dynamic regulation characteristics of electro-methanol conversion and output the scheduling scheme.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the computer program is executed, it implements the steps of the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program is configured to implement the steps of the method according to any one of claims 1 to 5 when invoked by a processor.

9. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 5.

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