A hydrogen storage-hydrogen transportation rolling optimization scheduling method and system for wind-solar hydrogen production

Abstract

The application discloses a hydrogen storage-hydrogen transmission rolling optimization scheduling method and system for wind-solar hydrogen production. The method constructs a dynamic model containing storage tank material balance, pressure state and hydrogen transmission capacity constraints, and supports time-varying working condition description of effective volume of the storage tank. In order to avoid optimization infeasibility, a total amount feasibility pre-check based on hydrogen production prediction, maximum hydrogen transmission capacity and hydrogen storage capacity of the storage tank is proposed, which is used for identifying over-tank risk in advance and triggering scheduling degradation. In view of the amplitude and frequency requirements of hydrogen transmission change of the downstream hydrogen utilization process, the method introduces integer variables and sliding windows to construct minimum adjustment frequency and minimum adjustment interval constraints. Based on real-time storage tank data and hydrogen production prediction, the method uses a mixed integer optimization algorithm to solve the optimal hydrogen transmission strategy in the rolling prediction time domain, and only executes the first step instruction of the current period, so that the rapid response to operation is realized. The application can improve the pressure safety margin of the storage tank, reduce the system adjustment frequency and enhance the operation stability of wind-solar hydrogen production.

Description

Technical Field

[0001] This invention relates to the field of optimization and scheduling technology for wind and solar power green hydrogen production systems, and in particular, to a rolling optimization scheduling method and system for hydrogen storage-transportation coordinated operation. Background Technology

[0002] With the continued advancement of the "dual carbon" goal, the proportion of renewable energy sources such as wind power and photovoltaics in the energy structure is constantly increasing. Utilizing renewable energy for water electrolysis to produce hydrogen has become an important component of the green and low-carbon transformation. In wind-solar hydrogen production systems, the electrolyzer, as a key upstream device, relies entirely on wind and solar power generation output. However, wind and solar resources are significantly affected by seasons, climate, and random disturbances, resulting in significant fluctuations and uncertainties in power generation, making it difficult to maintain a constant hydrogen production output from the electrolyzer. Because electrolyzers typically have minimum load limitations, dynamic response constraints, and efficiency that changes with load, their operating strategies must rely on the downstream hydrogen system for buffering and regulation.

[0003] Hydrogen storage tanks serve as a crucial buffer in the wind and solar hydrogen production process, connecting the unstable upstream hydrogen production with the relatively stable downstream demand. Their primary function is to absorb fluctuations in upstream hydrogen production and provide a stable supply to the downstream hydrogen transmission system. The internal pressure of the storage tank is directly affected by changes in hydrogen production. If the tank cannot release hydrogen in time during periods of high production, it can easily lead to excessively high pressure; conversely, if insufficient hydrogen is stored during periods of low production, it can result in excessively low pressure, failing to meet downstream hydrogen transmission and consumption demands. In actual engineering projects, parameters such as the effective volume of the storage tank, operating temperature, and gas compressibility also vary with operating conditions, making pressure variation patterns even more complex. Furthermore, some hydrogen production stations experience situations such as multiple tank switching, tank maintenance, and temporary tank shutdowns, causing the effective volume of the storage tank to exhibit time-varying characteristics, further increasing the difficulty of hydrogen storage scheduling.

[0004] Downstream of the storage tank, the hydrogen delivery system typically supplies hydrogen to hydrogen energy applications, such as hydrogen refueling stations, storage and transportation modules, or industrial hydrogen users, through compressors, pressure regulating valves, or pipelines. The hydrogen delivery capacity is affected by factors such as the rated flow rate of the equipment, pressure rating, dynamic response speed, and safety limitations, and its operating range has clearly defined upper and lower limits. Furthermore, because valves and pipelines can experience fatigue, wear, and even safety hazards during frequent adjustments, engineering practice generally requires that hydrogen delivery adjustments not be too frequent and that a certain minimum adjustment interval be observed. In addition, hydrogen delivery adjustments themselves can lead to increased energy consumption and amplified system fluctuations; therefore, actual operation requires minimizing the number of adjustments while ensuring pressure safety.

[0005] In summary, current technologies lack an integrated hydrogen storage and transportation scheduling method capable of simultaneously addressing issues such as hydrogen production fluctuations caused by wind and solar power fluctuations, time-varying tank volumes, hydrogen transport regulation constraints, future feasibility pre-checks, and cross-cycle regulation correlations. Therefore, it is necessary to propose a rolling optimization scheduling method with higher reliability, greater adaptability, and greater engineering feasibility to ensure the safety and stability of wind and solar green hydrogen production systems in complex operating environments. Summary of the Invention

[0006] The purpose of this invention is to address the problems in existing wind and solar green hydrogen production systems, such as the disconnect between hydrogen storage and transportation scheduling, excessive adjustment frequency, large pressure fluctuations in storage tanks, and difficulty in identifying operational infeasibility in advance. This invention provides a rolling optimization scheduling method and system for hydrogen storage and transportation in wind and solar hydrogen production, overcoming the shortcomings of existing technologies in terms of safety, stability, and economic efficiency. It achieves full-process coordinated optimization of hydrogen production from electrolyzers, pressure evolution in hydrogen storage tanks, and downstream hydrogen transportation capacity, thereby improving the continuity, predictability, and feasibility of system operation.

[0007] The objective of this invention is achieved through the following technical solution: In a first aspect, the present invention provides a rolling optimization scheduling method for hydrogen storage and transportation in wind and solar hydrogen production, comprising the following steps: (1) Construct a material balance model for a hydrogen storage-transportation system, which includes a dynamic equation for the amount of hydrogen in the storage tank and a dynamic model for the pressure in the storage tank; (2) Collect real-time data and wind and solar hydrogen production prediction sequences for multiple future time steps through the industrial communication protocol interface; (3) Conduct a feasibility pre-check based on the material balance model of the hydrogen storage-transportation system and the wind and solar hydrogen production prediction sequence to determine whether there is a feasible scheduling scheme that satisfies all physical constraints in the future prediction time domain. (4) When the feasibility pre-check is passed, set physical constraints for hydrogen storage and transportation in the future prediction time domain; (5) Set optimization objectives to minimize the frequency of hydrogen transport regulation, including constructing a minimum regulation frequency, a minimum regulation interval, and a cross-cycle regulation cooling mechanism; (6) Based on the material balance model of the hydrogen storage-transportation system described in step (1), the real-time data described in step (2), the physical constraints described in step (4), and the optimization objective described in step (5), the optimal hydrogen transport flow rate sequence for multiple future time steps is solved by a mixed integer programming algorithm, and the optimal hydrogen transport flow rate sequence for the current period is executed. (7) Based on the storage tank pressure, storage tank hydrogen quantity, hydrogen transmission flow rate and updated wind and solar forecast data after execution, repeat steps (1) to (6) to realize the rolling optimization scheduling of the hydrogen storage-transmission integrated system.

[0008] Furthermore, step (1) specifically includes the following sub-steps: (1.1) For the scenario of producing green hydrogen from wind and solar power, a dynamic equation for the amount of hydrogen in the storage tank is established in discrete time form based on the law of conservation of mass. The equation relates the amount of hydrogen in the storage tank, the hydrogen production flow rate of the electrolyzer, and the hydrogen transport flow rate at the current moment to the amount of hydrogen in the storage tank in the next cycle. (1.2) According to the ideal gas law, a dynamic model of the storage tank pressure is established. The model maps the amount of hydrogen in the storage tank, the storage tank temperature, and the effective volume at the next moment to the storage tank pressure at the same moment.

[0009] Furthermore, in step (2), the real-time data includes tank pressure, tank temperature, effective tank volume, real-time hydrogen production of the electrolyzer, and hydrogen transport flow rate executed in the previous cycle; the future multi-time-step wind and solar hydrogen production prediction sequence is generated from the wind and solar power generation prediction power of the future multiple time steps.

[0010] Further, step (3) specifically involves comparing the maximum storable hydrogen amount, the total future hydrogen inflow, the maximum future hydrogen outflow, and the maximum future inventory under the most unfavorable conditions in the future prediction time domain to determine whether the situation of exceeding the maximum capacity of the storage tank will occur during the prediction period; if it is determined that the future inventory may exceed the maximum capacity of the storage tank, the optimization solution of the current cycle is directly terminated, and the scheduling scheme of the previous cycle is used as the output instruction of the current cycle.

[0011] Furthermore, in step (4), the physical constraints specifically include: the hydrogen flow rate does not exceed the downstream capacity limit, the hydrogen flow rate variation does not exceed the set maximum adjustment amount, the tank pressure must be kept within a safe range, the tank hydrogen quantity must meet the material balance relationship, and the effective volume of the tank can change over time.

[0012] Step (5) includes the following sub-steps: Furthermore, step (5) includes the following sub-steps: (5.1) Introduce 0 / 1 adjustment flag variables to characterize hydrogen transport adjustment behavior. Set the minimum adjustment interval through sliding window constraints so that only one adjustment is allowed in any consecutive window, avoiding frequent adjustment and improving valve life and system stability; (5.2) Set up a regulation and cooling mechanism across rolling cycles so that hydrogen transport regulation is prohibited again within several time steps after regulation occurs in the previous cycle, so as to achieve the continuity and robustness of the regulation strategy.

[0013] (5.1) Introduce 0 / 1 adjustment flag variables to characterize hydrogen transport adjustment behavior. Set the minimum adjustment interval through sliding window constraints so that only one adjustment is allowed in any consecutive window, avoiding frequent adjustment and improving valve life and system stability; (5.2) Set up a regulation and cooling mechanism across rolling cycles so that hydrogen transport regulation is prohibited again within several time steps after regulation occurs in the previous cycle, so as to achieve the continuity and robustness of the regulation strategy.

[0014] Step (6) includes the following sub-steps: Furthermore, step (6) includes the following sub-steps: (6.1) Based on the material balance model of the hydrogen storage-transportation system, the real-time data, the physical constraints and the optimization objectives, the mixed integer programming algorithm is used to solve the problem and output the optimal hydrogen transport sequence in the future prediction time domain. (6.2) Only the first step of the optimal hydrogen transport sequence in the current cycle is executed, and the rest is used as a reference trajectory for optimization in the next cycle, so as to improve the responsiveness to real-time changes.

[0015] (6.1) Based on the material balance model of the hydrogen storage-transportation system, the real-time data, the physical constraints and the optimization objectives, the mixed integer programming algorithm is used to solve the problem and output the optimal hydrogen transport sequence in the future prediction time domain. (6.2) Only the first step of the optimal hydrogen transport sequence in the current cycle is executed, and the rest is used as a reference trajectory for optimization in the next cycle, so as to improve the responsiveness to real-time changes.

[0016] Secondly, the present invention provides a rolling optimization scheduling system for hydrogen storage and transportation in wind and solar hydrogen production, comprising: The model building module is used to build material balance models for hydrogen storage-transportation systems. The data acquisition module is used to acquire real-time data and future multi-time step wind and solar hydrogen production prediction sequences through an industrial communication protocol interface. The pre-check module is used to perform a scheduling feasibility pre-check based on the predicted sequence and the model; The constraint and target setting module is used to set physical constraints and optimization targets when the pre-check passes. The optimization and solution module is used to solve for the optimal hydrogen transport flow command sequence based on a mixed integer programming algorithm; The rolling execution module is used to execute the instructions of the current cycle and trigger the rolling optimization of the next cycle.

[0017] Thirdly, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the hydrogen storage-transportation rolling optimization scheduling method for wind and solar hydrogen production as described in the first aspect.

[0018] The beneficial effects of this invention are as follows: by adopting a unified optimization framework for the wind and solar green hydrogen production process, it is possible to adjust hydrogen storage and transportation behavior in real time, avoiding risks caused by excessively high or low tank pressure; by using a pre-check mechanism, it effectively reduces the risk of solution failure; by using a minimum adjustment frequency and a cooling mechanism, it significantly reduces the number of adjustment actions and extends equipment life; and by using rolling optimization, it improves the scheduling's adaptability to wind and solar power fluctuations, ultimately achieving safe, efficient, and economical green hydrogen production full-chain scheduling. Attached Figure Description

[0019] The objectives, features, and advantages of the present invention will be further understood through the following description of preferred embodiments in conjunction with the accompanying drawings. The present invention will be described in more detail below with reference to the accompanying drawings. However, the present invention can be embodied in many different forms and should not be considered as limited to the embodiments listed in the specification. Rather, such embodiments are provided to illustrate the implementation and completeness of the invention and to describe the specific implementation process of the invention to those skilled in the art.

[0020] Figure 1 This is a logical framework diagram of this method.

[0021] Figure 2 This is a multi-timestep wind and solar hydrogen production prediction sequence collected by the wind and solar power prediction model.

[0022] Figure 3 The graph shows the comparison between the hydrogen production of the electrolyzer and the hydrogen transport regulation behavior. Graph A shows the hydrogen production of the electrolyzer during this period, and Graph B shows the optimized calculation of the change in hydrogen transport flow rate. Detailed Implementation

[0023] The present invention will now be described in detail with reference to the accompanying drawings.

[0024] This invention discloses a rolling optimization scheduling method for hydrogen storage and transportation in wind and solar hydrogen production. The logical framework of the method is as follows: Figure 1 As shown, it includes the following steps: Step 1: For the wind and solar power green hydrogen production scenario, construct a material balance model for the hydrogen storage-transmission system. The material balance model for the hydrogen storage-transmission system includes the dynamic equation of hydrogen quantity in the storage tank and the dynamic model of pressure in the storage tank.

[0025] This invention employs a dynamic equation for hydrogen storage tank volume to describe the dynamic relationship between hydrogen production, storage, and transportation. This equation correlates the current hydrogen volume in the storage tank, the hydrogen production flow rate from the electrolyzer, and the hydrogen transportation flow rate with the hydrogen volume in the storage tank for the next cycle. The change in the hydrogen volume in the storage tank is described by the following expression: in, The amount of hydrogen in the storage tank for the next cycle. For a moment The amount of hydrogen in the storage tank, The amount of hydrogen produced by the electrolyzer. For downstream hydrogen supply, The sampling period.

[0026] The tank pressure is modeled based on the ideal gas law. The model maps the tank hydrogen quantity, tank temperature, and effective volume at the next moment to the tank pressure at the same moment. The model expression is as follows: in, For the next cycle of tank pressure, Standard atmospheric pressure As the compression factor, For the temperature of the storage tank, Standard temperature This refers to the effective volume of the storage tank as it changes over time.

[0027] Through the above two equations, this invention can accurately construct the pressure state evolution as the tank volume changes over time, supporting flexible operating conditions such as on-site maintenance. The model provides a precise description of the coupling relationship between hydrogen production, storage, and transportation, offering a unified mathematical framework for subsequent optimization.

[0028] Step 2: Collect real-time data and hydrogen production prediction data; Real-time operational data such as tank pressure, temperature, effective volume, electrolyzer hydrogen production, and previous cycle hydrogen transfer volume are collected via OPC or Modbus communication protocols. Hydrogen production prediction sequences for multiple future time steps are obtained based on external wind and solar power prediction models (e.g., LSTM, CNN, Transformer models).

[0029] Step 3: Conduct a feasibility pre-check. By comparing the maximum storable hydrogen capacity, total future hydrogen inflow, maximum future hydrogen outflow, and maximum future inventory under the most unfavorable conditions within the future forecast period, it is determined whether situations exceeding the storage tank's maximum capacity will occur during the forecast period. To avoid storage tank overpressure within the future forecast period, this invention proposes a total feasibility pre-check mechanism.

[0030] The maximum possible hydrogen storage capacity during the forecast period is: in, This represents the highest possible hydrogen storage capacity under the worst-case scenario in the forecast period. It is the predicted hydrogen production flow rate at time t+k. It is the predicted maximum transportable hydrogen flow rate at time t+k.

[0031] The maximum amount of hydrogen that can be accommodated during the future forecast period is: in, It predicts the maximum storable hydrogen capacity within the time domain. That is the maximum safe pressure of the storage tank. It is the time-varying effective volume of the storage tank, which represents the effective volume of the storage tank at time t+k in the future. It is the standard temperature. It is standard atmospheric pressure. It is the compression factor. It refers to the temperature inside the storage tank.

[0032] If the following relationship is satisfied: In this prediction time domain, overloading may inevitably occur. This invention directly skips the optimization solution and adopts the scheduling scheme of the previous cycle to ensure the safe and stable operation of the system.

[0033] Step 4: If the pre-inspection passes, establish key constraints including: hydrogen flow rate not exceeding downstream capacity limits; hydrogen flow variation not exceeding the set maximum adjustment amount; tank pressure maintained within a safe range; tank hydrogen quantity meeting material balance requirements; and the effective tank volume adaptable to time variations. This consistent constraint system ensures optimized scheduling is performed within existing equipment capabilities, safety boundaries, and operating limits.

[0034] Once the pre-check passes, the following scheduling constraints are established: The hydrogen transport flow rate shall not exceed the downstream capacity limit, i.e., the hydrogen transport flow rate constraint is as follows: in, and The upper and lower limits for hydrogen delivery at time t.

[0035] The variation in hydrogen transport volume shall not exceed the set maximum adjustment amount, i.e., the constraint on the variation in hydrogen transport volume is as follows: in, This represents the maximum adjustment range per step.

[0036] The pressure in the storage tank must be maintained within a safe range, i.e., the pressure constraints are as follows: in, and These are the upper and lower limits of the safe pressure range.

[0037] The hydrogen quantity in the storage tank must meet the material balance requirements, and the effective volume of the storage tank can change over time. The constraint formulas are as described in step one, including the dynamic equation for the hydrogen quantity in the storage tank and the dynamic model for the pressure in the storage tank in the material balance model of the hydrogen storage-transportation system.

[0038] A consistent constraint system ensures that optimized scheduling is performed within existing equipment capabilities, safety boundaries, and operational limits.

[0039] Step 5: Set the optimization objective of minimizing the frequency of hydrogen transport regulation, including constructing a minimum regulation frequency, a minimum regulation interval, and a cross-cycle regulation cooling mechanism; introduce a 0 / 1 regulation flag variable to characterize the hydrogen transport regulation behavior, and set the minimum regulation interval through a sliding window constraint to avoid frequent regulation and improve valve life and system stability. Simultaneously, set a cross-rolling cycle regulation cooling mechanism to prevent further hydrogen transport regulation within several time steps after regulation in the previous cycle, achieving continuity and robustness of the regulation strategy.

[0040] Specifically, the following steps are included: To reduce the frequent opening and closing of the control valve, this invention introduces a regulating flag variable: when A value of 1 indicates that the control valve is open. A value of 0 indicates that the control valve is closed.

[0041] To handle this in the optimization model, linearization using the large constant method is employed: in, To adjust the flag variable, It is a large constant.

[0042] The minimum adjustment interval limits continuous adjustment through a sliding window: in, For the minimum adjustment interval, The value of the flag variable is adjusted at time i.

[0043] Cross-cycle cooling mechanism setting cooling steps N cool and apply: in, For cooling steps, The hydrogen flow rate at time t+k in the future. This represents the hydrogen flow rate at the time step preceding time t+k. This mechanism can significantly reduce the frequency of adjustments and improve scheduling stability.

[0044] Step Six: Solve the optimization problem using a mixed-integer optimization algorithm; Based on the above model, constraints, and real-time data, a mixed-integer programming algorithm is used to solve for the optimal hydrogen flow sequence for multiple future time steps. Through a rolling optimization approach of "multi-step prediction, multi-step solution, and single-step execution," only the first step of the optimal sequence for the current period is executed, with the remaining parts serving as a reference trajectory for the next period's optimization, thereby improving the responsiveness to real-time changes. This invention employs a mixed-integer linear programming solver and executes a "multi-step solution, single-step implementation" strategy based on rolling optimization.

[0045] The optimization objective of this invention is to minimize the adjustment frequency. The optimal hydrogen transport flow rate sequence for the next multiple steps is obtained by solving the problem. Step 7: Execute the schedule and perform rolling updates; The system executes the optimal hydrogen flow rate for the current cycle, updates the hydrogen quantity and pressure in the storage tank, and then enters the next cycle to collect data again, re-execute pre-checks and optimization solutions, thereby achieving continuous rolling optimization scheduling.

[0046] like Figure 2 As shown in the figure, this is the future multi-time-step wind and solar hydrogen production prediction sequence constructed in this invention. The horizontal axis represents discrete time steps (each step represents 5 minutes), and the data covers a long-term prediction range of 960 time steps (corresponding to 80 hours). It can be seen that, affected by the intermittency of wind and solar resources, the hydrogen production ranges from 0 to 9000 Nm³. 3 The hydrogen flow rate fluctuates wildly between / h, exhibiting significant randomness and multi-peak characteristics, which poses a great challenge to the stability of downstream hydrogen transportation systems.

[0047] Figure 3 This illustrates the comparison between hydrogen production from the electrolyzer and the hydrogen transport regulation behavior. Figure 3A shows the hydrogen production from the electrolyzer during this period, and Figure 3B shows the optimized calculated change in hydrogen transport flow rate. Figure 3 It can be seen that although the upstream hydrogen production (Figure 3A) experienced a change from 1000 Nm³ in the interval between steps 20 and 60, 3 / h to 9000 Nm 3 Despite a significant increase in flow rate per hour, the scheduling algorithm of this invention, through constraints of "minimum adjustment frequency" and "sliding window," ensures that the change in downstream hydrogen flow rate (Figure 3B) remains zero for the vast majority of time steps. Throughout the entire 8-hour process, the system only triggered necessary valve adjustments twice, around steps 7 and 55, maintaining the valve opening locked for the remaining time. This demonstrates that this method significantly reduces mechanical wear on the hydrogen delivery valves.

[0048] This invention also provides a rolling optimization scheduling system for hydrogen storage and transportation in wind and solar hydrogen production, comprising: The model building module is used to build material balance models for hydrogen storage-transportation systems. The data acquisition module is used to acquire real-time data and future multi-time step wind and solar hydrogen production prediction sequences through an industrial communication protocol interface. The pre-check module is used to perform a scheduling feasibility pre-check based on the predicted sequence and the model; The constraint and target setting module is used to set physical constraints and optimization targets when the pre-check passes. The optimization and solution module is used to solve for the optimal hydrogen transport flow command sequence based on a mixed integer programming algorithm; The rolling execution module is used to execute the instructions of the current cycle and trigger the rolling optimization of the next cycle.

[0049] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the hydrogen storage-transportation rolling optimization scheduling method for wind and solar hydrogen production described in this invention.

[0050] This method is robust and innovative, with a clear step structure, facilitating computer programming implementation and offering high flexibility to adapt to various off-grid wind and solar hydrogen production application scenarios. This embodiment is one of the preferred implementations of the invention, but it does not constitute a limitation thereof. For those skilled in the art, various equivalent changes and improvements can be made without departing from the spirit of the invention, and all such changes and improvements are within the protection scope of the invention.

Claims

1. A rolling optimization scheduling method for hydrogen storage and transportation in wind and solar hydrogen production, characterized in that, Includes the following steps: (1) Construct a material balance model for a hydrogen storage-transportation system, which includes a dynamic equation for the amount of hydrogen in the storage tank and a dynamic model for the pressure in the storage tank; (2) Collect real-time operation data through industrial communication protocol interface; obtain wind and solar hydrogen production prediction sequence for multiple time steps in the future based on wind and solar power prediction model; (3) Based on the material balance model of the hydrogen storage-transportation system and the prediction sequence of wind and solar hydrogen production, a feasibility pre-check is performed to determine whether there is a feasible scheduling scheme that satisfies all physical constraints in the future prediction time domain; if it is determined that the storage tank may exceed the limit in the future, the scheduling result of the previous cycle is directly called and recursively calculated according to the time as the current scheduling output; (4) After the feasibility pre-check is passed, set the physical constraints for hydrogen storage and transportation in the future prediction time domain; (5) Set optimization objectives to minimize the frequency of hydrogen transport regulation, including constructing a minimum regulation frequency, a minimum regulation interval, and a cross-cycle regulation cooling mechanism; (6) Based on the material balance model of the hydrogen storage-transportation system described in step (1), the real-time data described in step (2), the physical constraints described in step (4), and the optimization objective described in step (5), the optimal hydrogen transport flow rate sequence for multiple future time steps is solved by a mixed integer programming algorithm, and the optimal hydrogen transport flow rate sequence for the current period is executed. (7) Based on the storage tank pressure, storage tank hydrogen quantity, hydrogen transmission flow rate and updated predicted hydrogen production data after execution, repeat steps (1) to (6) to realize the rolling optimization scheduling of the hydrogen storage-transmission integrated system.

2. The rolling optimization scheduling method for hydrogen storage and transportation based on wind and solar hydrogen production according to claim 1, characterized in that, Step (1) specifically includes the following sub-steps: (1.1) For the scenario of producing green hydrogen from wind and solar power, based on the law of conservation of mass, a dynamic equation for the amount of hydrogen in the storage tank is established in discrete time form. The equation relates the amount of hydrogen in the storage tank, the hydrogen production flow rate of the electrolyzer, and the hydrogen transport flow rate at the current moment to the amount of hydrogen in the storage tank in the next cycle. (1.2) Based on the ideal gas law, a dynamic model of tank pressure is established, wherein the model maps the amount of hydrogen in the tank, the temperature of the tank and the effective volume of the tank at the next moment to the tank pressure at the same moment.

3. The rolling optimization scheduling method for hydrogen storage and transportation based on wind and solar hydrogen production according to claim 1, characterized in that, In step (2), the real-time data includes tank pressure, tank temperature, effective tank volume, real-time hydrogen production of the electrolyzer, hydrogen flow rate of the previous cycle, and future hydrogen production prediction data; the future multi-time step wind and solar hydrogen production prediction sequence is generated by the wind and solar power generation prediction power of multiple future time steps.

4. The rolling optimization scheduling method for hydrogen storage and transportation based on wind and solar hydrogen production according to claim 1, characterized in that, The specific steps (3) are as follows: By comparing the maximum storable hydrogen amount, the total future hydrogen inflow amount, the maximum future hydrogen outflow amount and the maximum future inventory under the most unfavorable conditions in the future prediction time domain, it is determined whether there will be a situation that exceeds the maximum capacity of the storage tank during the prediction period. If it is determined that future inventory may exceed the maximum capacity of the storage tank, the optimization solution for the current cycle will be terminated directly, and the scheduling scheme of the previous cycle will be used as the output instruction for the current cycle.

5. The rolling optimization scheduling method for hydrogen storage and transportation based on wind and solar hydrogen production according to claim 1, characterized in that, In step (4), the physical constraints specifically include: the hydrogen flow rate does not exceed the downstream capacity limit, the hydrogen flow rate variation does not exceed the set maximum adjustment amount, the tank pressure must be kept within a safe range, the hydrogen quantity in the tank must meet the material balance relationship, and the effective volume of the tank can change over time.

6. The rolling optimization scheduling method for hydrogen storage and transportation based on wind and solar hydrogen production according to claim 1, characterized in that, Step (5) includes the following sub-steps: (5.1) Introduce 0 / 1 adjustment flag variables to characterize hydrogen transport adjustment behavior. Set the minimum adjustment interval through sliding window constraints so that only one adjustment is allowed in any consecutive window, avoiding frequent adjustment and improving valve life and system stability; (5.2) Set up a regulation and cooling mechanism across rolling cycles so that hydrogen transport regulation is prohibited again within several time steps after regulation occurs in the previous cycle, so as to achieve the continuity and robustness of the regulation strategy.

7. The rolling optimization scheduling method for hydrogen storage and transportation based on wind and solar hydrogen production according to claim 1, characterized in that, Step (6) includes the following sub-steps: (6.1) Based on the material balance model of the hydrogen storage-transportation system, the real-time data, the physical constraints and the optimization objectives, the mixed integer programming algorithm is used to solve the problem and output the optimal hydrogen transport sequence in the future prediction time domain. (6.2) Only the first step of the optimal hydrogen transport sequence in the current cycle is executed, and the rest is used as a reference trajectory for optimization in the next cycle, so as to improve the responsiveness to real-time changes.

8. A rolling optimization scheduling system for hydrogen storage and transportation in wind and solar hydrogen production, characterized in that, include: The model building module is used to build material balance models for hydrogen storage-transportation systems. The data acquisition module is used to acquire real-time data and future multi-time step wind and solar hydrogen production prediction sequences through an industrial communication protocol interface. The pre-check module is used to perform a scheduling feasibility pre-check based on the predicted sequence and the model; The constraint and target setting module is used to set physical constraints and optimization targets when the pre-check passes. The optimization and solution module is used to solve for the optimal hydrogen transport flow command sequence based on a mixed integer programming algorithm; The rolling execution module is used to execute the instructions of the current cycle and trigger the rolling optimization of the next cycle.

9. 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 processor executes the program, it implements the hydrogen storage-transportation rolling optimization scheduling method for wind and solar hydrogen production as described in any one of claims 1 to 7.

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