Method and related device for evaluating absorption capacity of green power chemical system

Abstract

The invention belongs to the technical field of new energy power generation and chemical production, and provides a green power chemical system consumption capability evaluation method and related device, and the method comprises the steps: collecting new energy power scene data, and generating a time sequence scene of wind power and photovoltaic output based on a long and short term sequence generative adversarial network; constructing an operation constraint model of the energy storage power station based on a time sequence scene of wind power and photovoltaic output; on the basis of the operation constraint model of the energy storage power station, constructing an operation constraint model of the hydrogen energy storage system; an operation constraint model of an energy storage power station and an operation constraint model of a hydrogen energy storage system are integrated, new energy consumption maximization and grid power demand minimization are taken as optimization targets, a green power chemical system consumption capability evaluation model considering new energy uncertainty is constructed, the model is solved by adopting a mathematical programming solver, and evaluation indexes are output. According to the method, the uncertainty of wind and light output is quantified through new energy scene generation, and the reliability of evaluation is improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of new energy power generation and chemical production, and particularly relates to a green electricity chemical system consumption capacity evaluation method and related device. BACKGROUND

[0002] Utilizing green electricity such as wind energy and solar energy to drive chemical production processes such as hydrogen production by electrolysis of water, ammonia synthesis, and methanol synthesis has become an important direction for the integrated development of energy and chemical industry. However, new energy has randomness and volatility, and its power output is unstable, which brings challenges to the chemical processes that require high continuity. The chemical process itself has complex reaction kinetics, energy conservation and material balance constraints, which need to be coupled with power fluctuations. Existing production simulation methods are mostly based on the assumption of deterministic power supply, and lack modeling and quantification of new energy output uncertainty. The dynamic adaptability, flexible operation interval, and consumption capacity of the new energy-chemical system cannot be comprehensively evaluated. SUMMARY

[0003] The purpose of the present application is to provide a green electricity chemical system consumption capacity evaluation method and related device to solve the problem that the prior art cannot comprehensively evaluate the dynamic adaptability, flexible operation interval, and consumption capacity of the new energy-chemical system.

[0004] To achieve the above-mentioned purpose, the following technical solutions are adopted in the present application: In a first aspect, the present application provides a green electricity chemical system consumption capacity evaluation method, comprising: Collecting new energy power scenario data, generating wind power and photovoltaic power output time series scenarios based on a long short-term sequence generative adversarial network; Based on the wind power and photovoltaic power output time series scenarios, an operation constraint model of an energy storage power station is constructed; Based on the operation constraint model of the energy storage power station, an operation constraint model of a hydrogen energy storage system is constructed; Integrating the operation constraint model of the energy storage power station and the operation constraint model of the hydrogen energy storage system, constructing a green electricity chemical system consumption capacity evaluation model considering new energy uncertainty, with the optimization objectives of maximizing new energy consumption and minimizing grid power demand, using a mathematical programming solver to solve the model, and outputting evaluation indexes.

[0005] Further, the collecting new energy power scenario data, generating wind power and photovoltaic power output time series scenarios based on a long short-term sequence generative adversarial network comprises: The network structure of wind power output scene generation based on long short-term sequence generative adversarial network is constructed, the generator structure is: MambaOUT module+three-layer convolutional neural network CNN; the discriminator structure is: MambaOUT module+self-attention mechanism+three-layer convolutional neural network CNN, wherein the MambaOUT module is used for short-term feature enhancement, the weight of short-term fluctuation feature in the input sequence is dynamically adjusted through the gating convolution mechanism, the self-attention mechanism is used for long-term dependence capture, the global dependence in the sequence is extracted, and the long-term feature of seasonality or climate law is captured.

[0006] Further, the time sequence scene based on wind power and photovoltaic output, the operation constraint model of the energy storage power station is constructed, including: charge and discharge power range constraint, charge and discharge state constraint, state of charge constraint, energy storage capacity range constraint and energy storage time constraint.

[0007] Further, specifically: charge and discharge power range constraint (1) (2) In the formula: represents the discharge power of the energy storage power station in the t time period, represents the charge power of the energy storage power station in the t time period, represents the discharge state of the energy storage power station in the t time period, which is a 0-1 variable, represents the charge state of the energy storage power station in the t time period, which is a 0-1 variable, and all the above are optimization variables, is the installed capacity of the energy storage power station; charge and discharge state constraint (3) state of charge constraint (4) In the formula: represents the energy storage capacity of the energy storage power station in the t time period, which is an optimization variable; and respectively represent the charge efficiency and discharge efficiency of the energy storage power station; energy storage capacity range constraint (5) In the formula: is the battery capacity of the energy storage power station. (5) In the formula: is the battery capacity of the energy storage power station.

[0008] ​​Furthermore, based on the operational constraint model of the energy storage power station, the construction of the operational constraint model of the hydrogen energy storage system includes: Constraints on hydrogen production by electrolyzers, power range constraints of electrolyzers, power generation constraints of fuel cells, capacity balance constraints of hydrogen storage devices, and external transmission capacity constraints of hydrogen storage devices.

[0009] Furthermore, specifically: Electrolyzer hydrogen production constraints (6) In the formula: Indicates that the electrolytic cell is in The amount of hydrogen produced during a given time period is the optimization variable. This indicates the hydrogen production efficiency of the electrolyzer; Electrolytic cell power range constraints (7) In the formula: Indicates that the electrolytic cell is in The running status of a time period is 0-1, which are optimization variables; and These represent the maximum and minimum technical output of the electrolytic cell, respectively. Fuel cell power generation constraints (8) In the formula: Indicating fuel cells in The amount of hydrogen consumed during each time period is the optimization variable; This indicates the power generation efficiency of the fuel cell; Fuel cell power generation range constraints (9) In the formula: Indicating fuel cells in The running status of a time period is 0-1, which are optimization variables; and These represent the maximum and minimum technical output of the fuel cell, respectively. Hydrogen storage tank hydrogen storage capacity range constraints (10) In the formula: and These represent the maximum and minimum capacity ranges of the hydrogen storage tank, respectively. Hydrogen storage tank hydrogen storage balance constraint (11) In the formula: The amount of hydrogen stored in the hydrogen storage tank. Hydrogen production capacity of the electrolyzer. For the hydrogen consumption of fuel cells, The amount of hydrogen transported through the hydrogen transport channel. This represents the initial hydrogen storage capacity of the hydrogen storage tank. Hydrogen transport power range constraints (12) In the formula: This is the minimum hydrogen transport capacity for the hydrogen transport channel. This represents the maximum hydrogen transport capacity of the hydrogen transport channel.

[0010] Furthermore, the integrated energy storage power station operation constraint model and the hydrogen energy storage system operation constraint model, with the optimization objectives of maximizing new energy consumption and minimizing grid power demand, construct a green energy chemical system consumption capacity assessment model considering the uncertainty of new energy. The model is solved using a mathematical programming solver, and the output evaluation indicators include: (13) In the formula: T represents the total length of the scheduling time; t is the simulation time step; The wind power output during time period t; The photovoltaic power output during time period t. For grid power demand, that is, the power supplied to the park by the main power grid during non-independent operation; The remaining constraints are shown in equations (14) to (16): Power balance constraints (14) In the formula: and Contribute to wind power and solar power, Net output for electrical energy storage, thermal energy storage and hydrogen energy storage, To meet grid power demand, The power loads are for chemical plants, including electro-hydrogen production, green ammonia production, PEM hydrogen production, alkaline water hydrogen production, and green methanol / green DMC production. for Load during a specific time period; Chemical load output constraint (15) In the formula: This is the rated load of the chemical plant. and These are the upper and lower limits of the adjustable range for chemical load; Chemical operating hours constraints (16) In the formula: This refers to the number of operating hours for the chemical load. The mathematical programming solver Cplex is called to solve the park production simulation optimization model, and the optimal solution of the optimization variable in each period and the optimal value of the objective function are obtained, and then the new energy utilization rate and off-grid independent operation hour index are obtained.

[0011] In a second aspect, the present application provides a green electricity chemical system consumption capacity evaluation system, comprising: A scene construction module is configured to collect new energy power scene data, generate wind power and photovoltaic output time sequence scenes based on a long short-term sequence generative adversarial network, and the like. A first constraint model construction module is configured to construct an operation constraint model of an energy storage power station based on the wind power and photovoltaic output time sequence scenes. A second constraint model construction module is configured to construct an operation constraint model of a hydrogen energy storage system based on the operation constraint model of the energy storage power station. An integrated output module is configured to integrate the operation constraint model of the energy storage power station and the operation constraint model of the hydrogen energy storage system, construct a green electricity chemical system consumption capacity evaluation model considering new energy uncertainty with the optimization objectives of maximizing new energy consumption and minimizing grid electricity demand, solve the model by using a mathematical programming solver, and output evaluation indexes.

[0012] In a third aspect, the present application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the green electricity chemical system consumption capacity evaluation method when executing the computer program.

[0013] In a fourth aspect, the present application provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program implements the steps of the green electricity chemical system consumption capacity evaluation method when executed by a processor.

[0014] Compared with the prior art, the present application has the following technical effects: The present application quantifies the uncertainty of wind and light output by generating new energy scenes, thereby improving the reliability of the evaluation. Secondly, the establishment of energy storage and hydrogen energy storage constraints ensures the feasibility and safety of system operation. The multi-objective optimization model takes into account the maximum new energy consumption and the minimum grid dependence, thereby improving the economic efficiency of the system. The model solving result can guide the actual park design, optimize chemical load scheduling and energy storage planning, thereby reducing carbon emissions and enhancing system flexibility. Overall, the method provides a scientific decision-making tool for a chemical park with high proportion of new energy access, and helps green energy consumption. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 The present application is a flowchart. DETAILED DESCRIPTION

[0016] The application is further illustrated below with reference to the accompanying drawings: Embodiment 1, please refer to Figure 1 The application provides a green electricity chemical system consumption capacity evaluation method, comprising: Collecting new energy power scene data, generating wind power and photovoltaic output time sequence scenes based on a long-short term sequence generative adversarial network; Based on the wind power and photovoltaic output time sequence scenes, an operation constraint model of an energy storage power station is constructed; Based on the operation constraint model of the energy storage power station, an operation constraint model of a hydrogen energy storage system is constructed; Integrating the operation constraint model of the energy storage power station and the operation constraint model of the hydrogen energy storage system, a green electricity chemical system consumption capacity evaluation model considering new energy uncertainty is constructed with maximization of new energy consumption and minimization of grid electricity demand as optimization objectives, a mathematical programming solver is used to solve the model, and evaluation indexes are output.

[0017] The application patent establishes operation models of wind, light, storage and chemical production objects, comprehensively considers maximization of new energy consumption and minimization of grid electricity demand as objectives, constructs a park production simulation optimization model of power-heavy chemical multi-system coupling, and realizes accurate simulation of heavy chemical flexible load and hydrogen storage / electricity storage operation plan. Production operation strategies under different new energy scenes can be provided, and decision-making basis for system design and dispatching is provided.

[0018] Embodiment 2, the application provides a green electricity chemical system consumption capacity evaluation method, comprising: 1. New energy annual power scene generation: The network structure of a wind power output scene generation method based on a long-short term sequence generative adversarial network is constructed, the generator structure is: MambaOUT module + three-layer convolutional neural network (CNN), and the discriminator structure is: MambaOUT module + self-attention mechanism + three-layer CNN, wherein the MambaOUT module is used for short-term feature enhancement, the weight of the short-term fluctuation feature in the input sequence is dynamically adjusted through a gating convolution mechanism, the fine generation capability of the model for sudden events such as power surge / drop is enhanced, the self-attention mechanism is used for long-term dependence capture, the global dependence in the sequence is extracted, and long-term features such as seasonality or climate regularity are captured.

[0019] 2. Establishing an energy storage power station operation constraint The energy storage power station operation constraint includes: charge-discharge power range constraint, charge-discharge state constraint, state of charge constraint, energy storage capacity range constraint and energy storage time length constraint, and specifically as follows: (1) Charge-discharge power range constraint (1) (2) In the formula: Indicates that the energy storage power station is Discharge power during the period Indicates that the energy storage power station is Charging power during the period Indicates that the energy storage power station is The discharge state during a given time period is a 0-1 variable. Indicates that the energy storage power station is The charging status during a given time period is represented by a 0-1 variable; all of these are optimization variables. This refers to the installed capacity of the energy storage power station.

[0020] (2) Charge and discharge state constraints (3) (3) Charge state constraints (4) In the formula: Indicates that the energy storage power station is The amount of electricity stored during a given time period is the optimization variable. and These represent the charging efficiency and discharging efficiency of the energy storage power station, respectively.

[0021] (4) Energy storage capacity range constraints (5) In the formula: This refers to the battery capacity of the energy storage power station.

[0022] 3. Establish operational constraints for hydrogen energy storage power plants.

[0023] The constraints on the operation of hydrogen energy systems include: constraints on hydrogen production by electrolyzers, constraints on the power range of electrolyzers, constraints on power generation by fuel cells, constraints on the capacity balance of hydrogen storage devices, and constraints on the external transmission capacity range of hydrogen storage devices.

[0024] (1) Constraints on hydrogen production from electrolyzers (6) In the formula: Indicates that the electrolytic cell is in The amount of hydrogen produced during a given time period is the optimization variable. This indicates the hydrogen production efficiency of the electrolyzer.

[0025] (2) Power range constraints of electrolytic cells (7) In the formula: Indicates that the electrolytic cell is in The running status of the time period is 0-1, which are optimization variables; and These represent the maximum and minimum technical output of the electrolytic cell, respectively.

[0026] (3) Constraints on fuel cell power generation (8) In the formula: Indicating fuel cells in The amount of hydrogen consumed during each time period is the optimization variable; This indicates the power generation efficiency of the fuel cell.

[0027] (4) Constraints on the power generation range of fuel cells (9) In the formula: Indicating fuel cells in The running status of the time period is 0-1, which are optimization variables; and These represent the maximum and minimum technical output of the fuel cell, respectively.

[0028] (5) Limitations on the hydrogen storage capacity of hydrogen storage tanks (10) In the formula: and These represent the maximum and minimum capacity ranges of the hydrogen storage tank, respectively.

[0029] (6) Hydrogen storage capacity balance constraints of hydrogen storage tanks (11) In the formula: The amount of hydrogen stored in the hydrogen storage tank. Hydrogen production capacity of the electrolyzer. For the hydrogen consumption of fuel cells, The amount of hydrogen transported through the hydrogen transport channel. This represents the initial hydrogen storage capacity of the hydrogen storage tank.

[0030] (7) Hydrogen transport power range constraints (12) In the formula: This is the minimum hydrogen transport capacity for the hydrogen transport channel. This represents the maximum hydrogen transport capacity of the hydrogen transport channel.

[0031] 4. Establish an assessment model for the green energy and chemical system's absorption capacity, taking into account the uncertainties of new energy sources. The optimization objective is to maximize new energy absorption while minimizing grid power demand within the optimization period. (13) In the formula: T represents the total length of the scheduling time; t is the simulation time step; The wind power output during time period t; Photovoltaic power generation output for time period t. Grid electricity demand, i.e. power supplied by the large power grid to the park during the non-independent operation period.

[0032] The remaining constraint conditions are shown in equations (14) to (16), including the following constraints, which are specifically described as follows.

[0033] (1) Power balance constraint (14) In the formula: and are wind power and photovoltaic output, is the net output of electric energy storage, thermal energy storage and hydrogen energy storage, is the grid electricity demand, is the chemical load power, including electric hydrogen, green ammonia, PEM hydrogen production, alkaline water hydrogen production, green methanol / green DMC, etc. is the load of the time period.

[0034] (2) Chemical load output constraint (15) In the formula: is the rated load of the chemical load, and are the upper limit of the adjustable range and the lower limit of the adjustable range of the chemical load.

[0035] (3) Chemical operating hours constraint (16) In the formula: is the operating hours of the chemical load.

[0036] The park production simulation optimization model is solved by calling the mathematical programming solver Cplex software, and the optimal solution of the optimization variables of each time period and the optimal value of the objective function can be obtained, and then the new energy utilization rate, off-grid independent operation hours and other indicators can be obtained. The production operation strategy under different scenarios is provided, which provides a decision basis for system design and dispatching.

[0037] The application quantifies the influence of new energy uncertainty on green electrification chemical system through multi-module coupling modeling. First, based on the generative adversarial network (GAN), wind and light output scenarios conforming to actual fluctuation rules are generated to solve the problems of new energy randomness and intermittency. Second, by establishing physical operation constraints of electric energy storage and hydrogen energy storage (such as charging and discharging power, capacity balance, etc.), the role of the energy storage system in suppressing fluctuations is described. Finally, taking the maximization of new energy consumption and the minimization of grid dependence as the goal, an electric power-chemical industry collaborative optimization model is constructed, and the system operation feasibility is ensured through constraint conditions (such as power balance and load adjustable range), so as to realize scientific evaluation of the consumption capacity.

[0038] Specifically, it is divided into four levels: New energy scenario generation: a GAN network composed of a generator (MambaOUT module + three-layer convolutional neural network CNN) and a discriminator (MambaOUT module + self-attention mechanism + three-layer CNN). Among them, the MambaOUT module strengthens the extraction of short-term power sudden change features through gated convolution, the self-attention mechanism (Self-Attention Mechanism) captures long-term dependencies such as seasonality, and generates time series scenarios with short-term fluctuations and long-term rules.

[0039] Electric energy storage modeling: the operation of energy storage is described through mathematical constraints, such as power range constraints to limit the upper and lower limits of power, state of charge constraints to associate energy storage capacity and efficiency, and charge and discharge state constraints to avoid simultaneous charging and discharging, to ensure that the model is consistent with the physical reality.

[0040] Hydrogen energy storage system modeling: covering the whole chain constraints of electrolytic cell, fuel cell and hydrogen storage tank. The electrolytic cell hydrogen production constraint is associated with power and hydrogen production, the hydrogen storage tank capacity balance constraint is coupled with hydrogen production, hydrogen consumption and external flow, and the equipment start-stop is controlled through 0-1 variable.

[0041] Optimization model solving: taking multi-objective function as the core, combining power balance constraints and chemical load adjustable range to build a mixed integer programming model, calling a solver such as Cplex to output optimization strategies at each time period, and finally obtaining key indicators such as consumption rate and off-grid hours.

[0042] The application integrates short-term feature enhancement and long-term dependence capture, and the generated scenarios are more consistent with the actual fluctuations of wind and light (such as sudden rise / sudden drop), significantly improving the reliability of the evaluation results.

[0043] The coupling modeling of electric energy storage and hydrogen energy storage constraints not only guarantees the safe operation of the system (such as preventing overcharging and overdischarging), but also enhances the flexibility of the system through the "production-storage-generation" chain of hydrogen energy, helping to consume new energy locally.

[0044] The present application can quantize the collaborative strategy of chemical load and energy storage in different scenarios by solving a multi-objective model, for example, adjusting the operation range of green ammonia and PEM hydrogen production load, effectively reducing the dependence on grid power, and providing dynamic scheduling basis for high-proportion new energy parks.

[0045] In another embodiment of the present application, a green chemical system consumption capacity evaluation system is provided, which can be used to realize the green chemical system consumption capacity evaluation method described above. The scene construction module is used to collect new energy power scene data, generate wind power and photovoltaic output time sequence scenes based on long and short term sequence generation confrontation network; The first constraint model construction module is used to construct the operation constraint model of the energy storage power station based on the time sequence scenes of wind power and photovoltaic output; The second constraint model construction module is used to construct the operation constraint model of the hydrogen energy storage system based on the operation constraint model of the energy storage power station; The integrated output module is used to integrate the operation constraint model of the energy storage power station and the operation constraint model of the hydrogen energy storage system, construct a green chemical system consumption capacity evaluation model considering new energy uncertainty, with the optimization objectives of maximizing new energy consumption and minimizing grid power demand, solve the model by using a mathematical programming solver, and output the evaluation index.

[0046] The division of modules in the embodiments of the present application is illustrative, and is only a logical function division. In actual implementation, there can be another division mode. In addition, the function modules in each embodiment of the present application can be integrated in one processor, or can be physically separated, or two or more modules can be integrated in one module. The integrated module can be realized in the form of hardware or in the form of a software function module.

[0047] In still another embodiment of the present application, a computer device is provided, which comprises a processor and a memory, the memory being configured to store a computer program, the computer program comprising program instructions, and the processor being configured to execute the program instructions stored in the computer storage medium. The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic, discrete hardware components, etc., which are the computing core and control core of the terminal, and are suitable for implementing one or more instructions, and are particularly suitable for loading and executing one or more instructions in the computer storage medium to implement a corresponding method flow or a corresponding function; the processor in the embodiments of the present application can be used for the operation of the green electric chemical system consumption capacity evaluation method.

[0048] In still another embodiment of the present application, the present application further provides a storage medium, specifically a computer readable storage medium (Memory), which is a memory device in a computer device, and is configured to store programs and data. It can be understood that the computer readable storage medium herein can include an internal storage medium in the computer device, and of course can also include an expansion storage medium supported by the computer device. The computer readable storage medium provides a storage space, and the storage space stores an operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and the instructions can be one or more computer programs (including program codes). It should be noted that the computer readable storage medium herein can be a high-speed RAM memory, or a non-volatile memory such as at least one disk memory. One or more instructions stored in the computer readable storage medium can be loaded and executed by the processor to implement the corresponding steps of the green electric chemical system consumption capacity evaluation method in the above embodiments.

[0049] Those skilled in the art will appreciate that embodiments of the application can be devised for a method, a system, or a computer program product. Accordingly, the present application can be embodied in the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer readable program code.

[0050] The present application is described in reference to the flowchart and / or block diagrams of the method, apparatus (system) and computer program product according to embodiments of the application. It will be understood that each block of the flowchart and / or block diagrams, and combinations of blocks in the flowchart and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processing device or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0051] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0052] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0053] Finally, it should be noted that the above-mentioned embodiments are merely intended for describing the technical solutions of the present application, but not for limiting it. Although the present application is described in detail with reference to the above embodiments, those skilled in the field should understand that the specific embodiments of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and any modification or equivalent replacement without departing from the spirit and scope of the present application should be covered in the protection scope of the claims of the present application.

Claims

1. A method for evaluating the accommodation capacity of a green electrochemical system, characterized in that, The method comprises the following steps: Collecting new energy power scene data, generating wind power and photovoltaic output time sequence scene based on long short-term sequence generative adversarial network; Based on the time sequence scene of wind power and photovoltaic output, the operation constraint model of energy storage power station is constructed; Based on the operation constraint model of energy storage power station, the operation constraint model of hydrogen energy storage system is constructed; Integrate the operation constraint model of energy storage power station and the operation constraint model of hydrogen energy storage system, take the maximization of new energy consumption and the minimization of grid electricity demand as the optimization objective, construct the green electricity chemical industry system consumption capacity evaluation model considering new energy uncertainty, solve the model by using mathematical programming solver, and output the evaluation index. 2.The method of claim 1, wherein, The method comprises the following steps: Construct the network structure of wind power output scene generation based on long short-term sequence generative adversarial network, the generator structure is: MambaOUT module+three-layer convolutional neural network CNN; The discriminator structure is: MambaOUT module+self-attention mechanism+three-layer convolutional neural network CNN, wherein the MambaOUT module is used for short-term feature enhancement, the weight of short-term fluctuation feature in the input sequence is dynamically adjusted through the gating convolution mechanism, the self-attention mechanism is used for long-term dependence capture, the global dependence in the sequence is extracted, and the long-term feature of seasonality or climate law is captured. 3.The method of claim 1, wherein, The method comprises the following steps: Charge and discharge power range constraint, charge and discharge state constraint, state of charge constraint, energy storage capacity range constraint and energy storage time length constraint.

4. The method according to claim 3, wherein, Specifically: Charge and discharge power range constraint （1） （2） In the formula: Indicates that the energy storage power station is Discharge power during the period Indicates that the energy storage power station is Charging power during the period Indicates that the energy storage power station is The discharge state during a given time period is a 0-1 variable. Indicates that the energy storage power station is The charging status during a given time period is represented by a 0-1 variable; all of these are optimization variables. The installed capacity of the energy storage power station; Charge and discharge state constraint （3） State of charge constraint （4） In the formula: represents the energy storage station at the time period, is an optimization variable; and respectively represent the charging efficiency and the discharging efficiency of the energy storage station; Energy storage capacity range constraint （5） In the formula: is the energy storage plant battery capacity.

5. The method of claim 1, wherein the method further comprises: determining the consumption capacity of the green electric chemical system based on the power consumption of the green electric chemical system. The method comprises the following steps: Electrolytic cell hydrogen production constraint, electrolytic cell power range constraint, fuel cell power generation constraint, hydrogen storage device capacity balance constraint, hydrogen storage device external sending capacity range constraint.

6. The method according to claim 5, wherein, Specifically: Electrolytic cell hydrogen production constraint （6） wherein: represents the amount of hydrogen produced by the electrolyzer during the time period, which is an optimization variable, represents the amount of hydrogen produced by the electrolyzer during the time period, which is an optimization variable, represents the efficiency of the electrolyzer in producing hydrogen; Electrolytic cell power range constraint （7） wherein: represents the operating state of the electrolysis cell at the time interval, being a 0-1 optimization variable; and represent the maximum and minimum technical power of the electrolysis cell, respectively. Fuel cell power generation constraint （8） In the formula: represents the hydrogen consumption of the fuel cell in the time period, which is an optimization variable; represents the power generation efficiency of the fuel cell; Fuel cell power generation power range constraint （9） wherein: represents the operating state of the fuel cell at is the time period, is a 0-1 optimization variable; and represent the maximum and minimum technical power of the fuel cell, respectively. Hydrogen storage tank hydrogen storage amount range constraint （10） wherein: and respectively represent the maximum and minimum capacity range of the hydrogen storage tank; Hydrogen storage tank hydrogen storage amount balance constraint （11） In the formula: is the hydrogen storage amount of the hydrogen storage tank, is the hydrogen production amount of the electrolytic tank, is the hydrogen consumption amount of the fuel cell, is the hydrogen transmission amount of the hydrogen transmission channel, is the initial hydrogen storage amount of the hydrogen storage tank; Hydrogen transmission power range constraint （12） In the formulae: is the minimum hydrogen transport amount of the hydrogen transport channel, is the maximum hydrogen transport amount of the hydrogen transport channel.

7. The method according to claim 1, wherein, The method comprises the following steps: （13） In the formula, T represents the total length of the scheduling time; t is the simulation time step; is the wind power output of the time period t; is the photovoltaic power output of the time period t; is the grid power demand, i.e., the power supplied by the large power grid to the park during the non-independent operation period; Integrate the operation constraint model of energy storage power station and the operation constraint model of hydrogen energy storage system, take the maximization of new energy consumption and the minimization of grid electricity demand as the optimization objective, construct the green electricity chemical industry system consumption capacity evaluation model considering new energy uncertainty, solve the model by using mathematical programming solver, and output the evaluation index, comprising: The remaining constraint conditions are shown in formulas (14) to (16): （14） In the formula: and is the wind power, photovoltaic power output, is the net output of electric energy storage, thermal energy storage and hydrogen energy storage, is the grid electricity demand, is the chemical load power, such as e-hydrogen, green ammonia, PEM hydrogen production, alkali water hydrogen production, green methanol / green DMC, and other chemical load types, is the load of the period; Power balance constraint （15） In the formulae: is the rated load of the chemical load, and is the upper limit of the adjustable range and the lower limit of the adjustable range of the chemical load; Chemical load output constraint （16） In the formula: is the number of operating hours of the chemical load; Chemical operation hour constraint 8. A green electrochemical system accommodation capacity evaluation system, characterized in that, Call the mathematical programming solver Cplex to solve the park production simulation optimization model, obtain the optimal solution of each period optimization variable and the optimal value of the objective function, and then obtain the new energy utilization rate and off-grid independent operation hour index. The method comprises the following steps: A scene construction module is used to collect new energy power scene data, and generate wind power and photovoltaic output time sequence scene based on long short-term sequence generative adversarial network; The first constraint model construction module is configured to construct an operation constraint model of the energy storage power station based on time sequence scenarios of wind power and photovoltaic output; The second constraint model construction module is configured to construct an operation constraint model of the hydrogen energy storage system based on the operation constraint model of the energy storage power station; The integration output module is configured to integrate the operation constraint model of the energy storage power station and the operation constraint model of the hydrogen energy storage system, construct a green electricity chemical industry system consumption capacity evaluation model considering new energy uncertainty, and take maximization of new energy consumption and minimization of grid electricity demand as optimization objectives, and solve the model by using a mathematical programming solver to output evaluation indexes.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the steps of the green electricity chemical industry system consumption capacity evaluation method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program is executed by the processor to implement the steps of the green electricity chemical industry system consumption capacity evaluation method according to any one of claims 1 to 7.

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