Multi-objective hierarchical rolling optimization scheduling method and system for off-grid wind-solar green hydrogen production

Abstract

The present invention discloses a multi-objective hierarchical rolling optimization scheduling method and system for off-grid wind-solar green hydrogen production. The method first establishes a total power balance model for the off-grid wind-solar hydrogen production scenario, and secondly, establishes the charging and discharging models of the energy storage device respectively, and introduces 0 and 1 integer state variables to unify the description of the independent charging and discharging models, and then sets physical constraints respectively. Finally, the method establishes a multi-objective hierarchical optimization proposition and uses a mixed 0-1 integer nonlinear programming algorithm to optimize and solve based on the models, constraints, and real-time wind-solar power generation and future power forecast data established in the aforementioned steps. The system developed based on this method can ensure the dynamic optimization of the scheduling strategy through the prediction model and optimization rolling update scheduling plan based on the real-time and predicted wind-solar power generation data and the high and low priority optimization goals set by the operating personnel, and provide reliable operation guidance for wind-solar hydrogen production dispatchers in real time.

Description

Technical Field

[0001] The present invention relates to the field of new energy system optimization and scheduling technology, and in particular, to a multi-objective hierarchical rolling optimization scheduling method and system for off-grid wind-solar green hydrogen production. Background Art

[0002] In off-grid wind and solar hydrogen production scenarios, the system typically becomes an "island," operating independently of the grid and relying entirely on fluctuating power from wind and photovoltaic power generation. However, both wind and photovoltaic power generation exhibit significant randomness, intermittency, and volatility, which directly impacts the stability of hydrogen production in the electrolyzer. Since off-grid systems cannot rely on the grid to balance fluctuating supply and demand, power balancing and allocation scheduling within the system itself are particularly important. During the hydrogen production process, the electrolyzer requires a relatively stable power input, and power fluctuations can affect hydrogen production efficiency and equipment lifespan. While the introduction of energy storage devices can mitigate these fluctuations to a certain extent, they also further increase system complexity. Furthermore, due to the fluctuating nature of wind and solar power generation and the unstable power demand of the hydrogen production process, power supply may exceed demand, resulting in some power generation being abandoned due to insufficient absorption capacity of energy storage and hydrogen production equipment. This significantly limits efficient resource utilization.

[0003] Therefore, in order to maximize energy utilization efficiency and hydrogen production, it is necessary to establish a multi-objective hierarchical optimization model based on the dynamic characteristics of wind and solar power generation, and rationally adjust the power balance of wind and solar power generation, energy storage system and electrolyzer, so as to minimize power abandonment, maximize hydrogen production and optimize the economy and energy efficiency of system operation on the basis of safe operation. Summary of the Invention

[0004] The purpose of the present invention is to address the shortcomings of the existing technology and provide a multi-objective hierarchical rolling optimization scheduling method and system for off-grid wind-solar green hydrogen production, so as to overcome the shortcomings of the off-grid wind-solar hydrogen production system in the existing technology in terms of fluctuating new energy utilization and hydrogen production stability, and realize dynamic optimization and efficient coordination of the entire chain of wind-solar power generation and green hydrogen production.

[0005] The object of the present invention is achieved through the following technical solution: providing a multi-objective hierarchical rolling optimization scheduling method for off-grid wind-solar green hydrogen production, comprising the following steps:

[0006] (1) Establishing a total power balance model for an off-grid wind-solar hydrogen production scenario, wherein the total power balance model is used to describe the off-grid wind-solar hydrogen production scenario;

[0007] (2) Establish a dynamic model of the energy storage device and introduce variables to describe the independent charging and discharging models into a unified mathematical model;

[0008] (3) Based on the actual requirements of wind-solar hydrogen production scenarios, set physical constraints for the decision variables of the energy storage devices involved in the model;

[0009] (4) Based on the actual needs of wind and solar hydrogen production scenarios, set the goals of coverage level optimization propositions, including minimum power abandonment, maximum hydrogen production and high energy utilization, and set priorities for the optimization goals;

[0010] (5) Read the real-time wind and solar power generation data and the future multi-step power forecast data provided by the upstream through the industrial communication protocol;

[0011] (6) Based on the data read in step (5), the optimization proposition set in step (4), the model in step (2), and the physical constraints of each decision variable in step (3), a mixed 0-1 integer nonlinear programming algorithm is used to perform optimization and solution. The optimized scheduling instruction obtained contains multiple beats, but only the first beat will be executed, and the optimal scheduling decision instruction for the first beat is obtained;

[0012] (7) Execute the first optimization scheduling instruction in step (6), and in the next optimization cycle, re-optimize and solve according to the latest hydrogen production feedback data to achieve continuous optimization.

[0013] Furthermore, the total power balance model in step (1) is used to describe the off-grid wind-solar hydrogen production scenario, specifically including the wind-solar power generation power, the charge and discharge power of the energy storage device, the electrolyzer power, the abandoned power and the charge and discharge status of the energy storage device in the off-grid wind-solar hydrogen production scenario.

[0014] Furthermore, the step (1) specifically includes the following sub-steps:

[0015] (1.1) Establish a total power balance model for the off-grid wind-solar hydrogen production system, incorporating wind-solar power generation, energy storage system charge and discharge power and status, electrolyzer power, and curtailed power into the model to fully describe the interactions between various power nodes;

[0016] (1.2) By setting power flow conditions under different operating conditions, a dynamic power balancing mechanism covering wind and solar power generation, energy storage, and hydrogen production systems is formed, so that the system meets the balance conditions of power input and consumption at all times.

[0017] Furthermore, the step (2) is specifically as follows:

[0018] (2.1) Establish a dynamic charging model and a dynamic discharging model for the energy storage device. The dynamic charging model and the dynamic discharging model of the energy storage device can simultaneously describe the dynamic charging and discharging behaviors by introducing discrete 0 and 1 integer state variables;

[0019] (2.2) By switching the state variables of the energy storage device, the energy storage device can seamlessly switch between charging and discharging behaviors, while ensuring that the model can describe the changing path of the system's charging and discharging behavior in a unified mathematical model.

[0020] Furthermore, the constraints in step (3) are physical safety constraints in actual scenarios, which are based on the physical characteristics of the energy storage system and the electrolyzer equipment, and then set key constraints. They are hard constraints that cannot be relaxed, and the decision variables covering the energy storage device include the upper and lower limits of the state of charge, the charge and discharge rate limit, the electrolyzer input power limit, and the maximum power change allowed for the electrolyzer within the safety range; these constraints are all incorporated into the scheduling model as inequality conditions to ensure that the scheduling optimization operates within the safety boundary.

[0021] Furthermore, the step (4) specifically includes the following sub-steps:

[0022] (4.1) Based on power balance and physical constraints, a multi-objective hierarchical optimization model is constructed with the core objectives of minimizing curtailed power, maximizing hydrogen production, and achieving high energy utilization;

[0023] (4.2) By assigning weights to each optimization objective and setting a hierarchical optimization structure, the multi-objective model can achieve hierarchical allocation of priorities to meet the differentiated needs of scheduling objectives.

[0024] Furthermore, the step (5) is specifically as follows: collecting the current power data of wind and solar power generation in real time through the industrial communication protocol interface, and combining it with the power generation forecast information of multiple time steps in the future as the input data of the rolling optimization; the real-time data and the forecast data are used as the time series variables of the model, so that the system can schedule the energy storage charging and discharging and the electrolyzer input power in advance according to the change trend of the power generation.

[0025] Furthermore, the step (6) is specifically as follows: using a mixed 0-1 integer nonlinear programming algorithm to solve the multi-objective optimization model. Since the nonlinear optimization proposition involves 0 and 1 integer variables, it is necessary to use a mixed integer nonlinear programming algorithm to solve it. The solved optimization scheduling instruction contains multiple beats, but only the first beat will be executed; the algorithm also considers the power changes and constraints at future moments, adopts a step-by-step solution method, and optimizes the objective function value in each time step according to the input power data, thereby ensuring the best match between the current scheduling decision and the real-time data.

[0026] Furthermore, the step (7) is specifically as follows: in a new optimization cycle, the optimization algorithm needs to re-solve the mixed 0-1 integer nonlinear programming problem based on the latest wind and solar power data, energy storage device status, power, and real-time operating power information of the electrolyzer, and still only execute the scheduling optimization instruction of the first beat; that is, in each rolling cycle, the optimal scheduling instruction of the first time step of the current cycle is executed; by executing the first scheduling instruction, the equipment operation instructions such as the energy storage charging and discharging power and the electrolyzer input power are adjusted, and at the same time, the latest data is re-collected in the next optimization cycle and the solution steps are repeated, so as to adjust the scheduling plan according to the latest system status and data; through rolling optimization, dynamic adjustment of the scheduling plan is achieved, so that the system scheduling can respond to real-time data changes in a timely manner and achieve continuous optimization.

[0027] The present invention also discloses a multi-objective hierarchical rolling optimization scheduling system for off-grid wind-solar green hydrogen production, comprising the following units:

[0028] Establishing a total power balance model unit: establishing a total power balance model for an off-grid wind-solar hydrogen production scenario, wherein the total power balance model is used to describe the off-grid wind-solar hydrogen production scenario;

[0029] Establish dynamic and mathematical model units: Establish a dynamic model of the energy storage device and introduce variables to describe the independent charging and discharging models into a unified mathematical model;

[0030] Setting physical constraint units: Based on the actual requirements of wind-solar hydrogen production scenarios, set physical constraints for the decision variables of the energy storage devices involved in the model;

[0031] Setting optimization proposition unit: Based on the actual needs of wind and solar hydrogen production scenarios, set the goals of coverage-level optimization propositions, including minimum power curtailment, maximum hydrogen production, and high energy utilization, and set priorities for the optimization goals;

[0032] Power prediction unit: reads real-time wind and solar power generation data and future multi-step power prediction data provided by upstream through industrial communication protocols;

[0033] Optimization solving unit: Based on the data read by the power prediction unit, the optimization proposition set in the optimization proposition unit, the model established in the dynamic and mathematical model units, and the physical constraints of each decision variable in the physical constraint unit, the optimization solution is performed using a mixed 0-1 integer nonlinear programming algorithm. The solved optimization scheduling instruction contains multiple beats, but only the first beat will be executed to obtain the optimal scheduling decision instruction for the first beat;

[0034] Continuous optimization unit: Executes the first-shot optimization scheduling instruction in the optimization solution unit, and in the next optimization cycle, re-optimizes and solves according to the latest hydrogen production feedback data to achieve continuous optimization.

[0035] The beneficial effects of the present invention are as follows:

[0036] The method of the present invention intelligently and dynamically adjusts the energy storage system's charge and discharge power, state of charge, and electrolyzer input power based on real-time wind and solar power generation and forecast data, achieving dynamic balance and efficient scheduling in green hydrogen production. The rolling optimization mechanism designed into this method ensures the system rapidly responds to fluctuations in wind and solar power generation, reduces curtailment losses, increases hydrogen production, and optimizes energy utilization efficiency. Ultimately, it achieves optimal scheduling decisions in terms of economy and energy efficiency, providing strong technical support for off-grid green hydrogen applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The objects, features, and advantages of the present invention can be further understood by describing preferred embodiments of the present invention 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 may be implemented in many different forms and should not be considered limited to the embodiments set forth in the specification. Rather, such embodiments are provided to illustrate the implementation and completeness of the present invention and to describe the specific implementation process of the present invention to those skilled in the art.

[0038] Figure 1 It is the algorithm logic framework diagram of the present invention;

[0039] Figure 2 Power forecast data for renewable energy wind and solar power generation;

[0040] Figure 3 The simulation results of the case study of this method are shown in Figure 2. DETAILED DESCRIPTION

[0041] The present invention will be described in detail below with reference to the accompanying drawings.

[0042] The present invention discloses a multi-objective hierarchical rolling optimization scheduling method and system supporting off-grid wind and solar green hydrogen production, such as Figure 1 As shown, the following steps are included:

[0043] Step 1: Build a system total power balance model

[0044] For off-grid wind and solar hydrogen production scenarios, a comprehensive power balance model was designed that includes key nodes such as wind and solar power generation, energy storage systems, electrolyzers, and curtailed power. In this model, fluctuations in wind and photovoltaic power generation are dynamically balanced through the charging and discharging behavior of the energy storage system to maintain a stable power input. This balance model also introduces a curtailed power variable to describe the flow of excess power in the system under power surplus conditions. The model is expressed as:

[0045] ;

[0046] in Indicates time, For new energy power generation, is the discharge power of the energy storage device, is the power consumed by the electrolytic cell, Charging power for energy storage device, is the abandoned power, satisfying:

[0047] ;

[0048] Since the battery energy storage device cannot be charged and discharged at the same time, the state variable is introduced , Represent the charge and discharge states of the battery respectively. Further, the total power balance update model in formula (1) can be modified as follows:

[0049] ;

[0050] in , A binary variable of 0 or 1. , ; In the discharge state, , This model ensures that the system power flow is closed-loop balanced under different power input conditions.

[0051] Step 2: Construct a unified mathematical model of energy storage charging and discharging behavior:

[0052] A mathematical model for the energy storage system's charge and discharge is constructed and combined into a single model expression by introducing integer state variables of 0 and 1. At each time step, the charge and discharge behavior is regulated by the state variables, enabling seamless switching between charge and discharge modes for the energy storage system, making a single model applicable to both charging and discharging scenarios. The expressions for the energy storage device's charge, discharge, and unified charge and discharge model are shown below, where the charge model is:

[0053] ;

[0054] The discharge model is:

[0055] ;

[0056] Introducing 0 and 1 state variables Finally, the unified charge and discharge model is:

[0057] ;

[0058] in Indicates that the energy storage device is at the current time step The state of charge, which is expressed as a percentage or a ratio relative to the total battery capacity, is used to describe the remaining battery capacity; Indicates the current time step The time interval is used to standardize the effect of charge and discharge power on the state of charge; Indicates that the energy storage device is at the current time step The rated capacity indicates the total stored energy of the battery when fully charged. and They represent the charging and discharging efficiency of the energy storage device, respectively, and are used to reflect the degree of energy conversion loss during the charging and discharging process.

[0059] Step 3: Set the physical constraints of energy storage and hydrogen production equipment;

[0060] Combined with the characteristics of the system equipment, key physical constraints are set for the energy storage device and electrolyzer to ensure safe and stable operation of the system. Specifically, they include:

[0061] Charge and discharge rate constraints of energy storage devices:

[0062] ;

[0063] in represent the lower and upper power constraints for charging and discharging, respectively.

[0064] State of charge constraints of energy storage devices:

[0065] ;

[0066] in They represent the lower and upper limits of the state of charge of the energy storage device respectively.

[0067] Electrolyzer input power limit:

[0068] ;

[0069] in They represent the lower and upper power limits allowed under the safe operation state of the electrolyzer.

[0070] Electrolyzer input power fluctuation limit:

[0071] ;

[0072] in They respectively represent the lower and upper limits of power fluctuation allowed under the safe operation state of the electrolyzer.

[0073] Step 4: Construct a multi-stage hierarchical optimization objective:

[0074] Based on the power balance model and physical constraints, a multi-objective optimization model is constructed with the core objectives of minimizing power curtailment rate, maximizing hydrogen production and maximizing energy utilization efficiency. The optimization objectives are as follows: The amount of wind and solar power curtailment in the hour is minimized, and on this basis, the allocated power of the electrolyzer is maximized (the power subsequently used for water electrolysis to produce hydrogen is the largest). The corresponding objective function shows an optimization scenario in the following form:

[0075] ;

[0076] ;

[0077] in represents the sampling frequency, represents the goal of minimizing curtailed power, represents the goal of maximizing the electrolyzer power generation, Indicates the current moment, Represents the optimization time domain, and the optimization objective function expression is and Constitute a hierarchical optimization problem, the objective function expression has a higher priority than the objective function expression , solve the objective function expression The obtained solution is used as the objective function expression Constraints, Represents the decision variable, and the optimization objective function expression is as follows:

[0078] ;

[0079] In addition, the optimization goal of this step in the scheduling system can be adjusted at any time according to site needs, including priority.

[0080] Step 5: Real-time and forecast data collection of upstream wind and solar power generation

[0081] Using industrial communication protocols (common protocols such as OPC and Modbus are available), current wind and solar power generation is collected in real time, along with multi-step power forecasts. This forecast data, derived from upstream model power estimates and combined with real-time data, serves as a key input for rolling optimization scheduling. This future forecast data enables the model to perceive future trends, making energy storage and electrolyzer power scheduling more predictable and forward-looking.

[0082] Step 6: Rolling optimization algorithm solution;

[0083] The hierarchical optimization problem in step 4 is solved using a mixed 0-1 integer nonlinear programming method. In the current cycle, based on the power forecast and real-time data of multiple future time steps, a set of scheduling instructions for the current cycle is generated, and only the optimal scheduling solution for the first time step is executed. This method ensures that the scheduling strategy is closely aligned with the actual system requirements and is optimized in real time through rolling updates. and Taking the optimization goal in as an example, the optimization proposition solved by the optimization algorithm can be expressed as follows:

[0084] Solving the first stage optimization problem:

[0085] ;

[0086] The optimal goal of the first stage is recorded as , then the optimization proposition of the second stage is:

[0087] ;

[0088] ;

[0089] The decision instructions for the next multiple steps to solve the above hierarchical optimization problem are recorded as :

[0090] ;

[0091] in is the scheduling decision instruction actually executed. It should be noted that if the above multi-stage optimization problem can be successfully solved, the optimal instruction of the last stage will be returned; if the solution of a stage fails, the optimal instruction of the previous stage will be output; if the problem of the first stage fails to be solved, the optimal instruction of the previous optimization moment will be zero-order held.

[0092] Step 7: Execute scheduling and rolling update;

[0093] During each rolling cycle, the dispatch instructions for the first time step are executed. In the next cycle, the optimization problem is re-solved and new dispatch instructions are generated based on the latest wind and solar power generation data, energy storage status, electrolyzer power requirements, and other information. This rolling optimization allows the system to update dispatch decisions in real time within each cycle and respond quickly to fluctuations in wind and solar power generation.

[0094] like Figure 2 As shown in the figure, this is the wind and solar power generation power curve of renewable energy read from the upstream. The curve shows the forecast data for the next 24 hours, which is provided by the upstream (this figure takes photovoltaic power generation as an example). Figure 3The simulation result diagram shows the charging power curve, discharging power curve, electrolytic cell power curve, abandoned power curve and state of charge curve of the energy storage device dispatched by the present invention based on the new energy power generation data. Figure 2 and Figure 3 As can be seen, during the off-peak period of renewable energy generation (12:00-5:00), the system maintains operation through energy storage discharge. During this period, the SOC (state of charge) of the energy storage device shows a reasonable downward trend, and no power curtailment occurs. During the peak generation period around 12:00, the system prioritizes allocating excess power to the electrolyzer and initiates energy storage charging, causing the SOC to steadily increase. Only a small amount of power curtailment occurs when power generation exceeds the system's capacity. This dynamic scheduling mechanism achieves three optimization goals: first, minimizing curtailment through precise regulation of energy storage charging and discharging; second, maximizing electrolyzer operating power during periods of abundant power generation to improve hydrogen production efficiency; and finally, ensuring the continuous power supply capacity of the energy storage system through scientific management of SOC. This demonstrates that the present invention can rationally dispatch the power of various devices in the hydrogen production system (energy storage device charging and discharging power, electrolyzer power) based on the power generated by renewable energy wind and solar power, thereby minimizing wind and solar curtailment and maximizing the allocated power of the electrolyzer.

[0095] The present invention also discloses a multi-objective hierarchical rolling optimization scheduling system for off-grid wind-solar green hydrogen production, comprising the following units:

[0096] Establishing a total power balance model unit: establishing a total power balance model for an off-grid wind-solar hydrogen production scenario, wherein the total power balance model is used to describe the off-grid wind-solar hydrogen production scenario;

[0097] Establish dynamic and mathematical model units: Establish a dynamic model of the energy storage device and introduce variables to describe the independent charging and discharging models into a unified mathematical model;

[0098] Setting physical constraint units: Based on the actual requirements of wind-solar hydrogen production scenarios, set physical constraints for the decision variables of the energy storage devices involved in the model;

[0099] Setting optimization proposition unit: Based on the actual needs of wind and solar hydrogen production scenarios, set the goals of coverage-level optimization propositions, including minimum power curtailment, maximum hydrogen production, and high energy utilization, and set priorities for the optimization goals;

[0100] Power prediction unit: reads real-time wind and solar power generation data and future multi-step power prediction data provided by upstream through industrial communication protocols;

[0101] Optimization solving unit: Based on the data read by the power prediction unit, the optimization proposition set in the optimization proposition unit, the model established in the dynamic and mathematical model units, and the physical constraints of each decision variable in the physical constraint unit, the optimization solution is performed using a mixed 0-1 integer nonlinear programming algorithm. The solved optimization scheduling instruction contains multiple beats, but only the first beat will be executed to obtain the optimal scheduling decision instruction for the first beat;

[0102] Continuous optimization unit: Executes the first-shot optimization scheduling instruction in the optimization solution unit, and in the next optimization cycle, re-optimizes and solves according to the latest hydrogen production feedback data to achieve continuous optimization.

[0103] This method is innovative and has a clear step structure. It is easy to implement in a computer program and has strong flexibility. It can adapt to a variety of off-grid wind and solar hydrogen production application scenarios. This embodiment is one of the preferred implementations of the present invention, but it does not limit the present invention. For those skilled in the art, various equivalent changes and improvements can be made without departing from the spirit of the present invention, and these changes and improvements are all within the scope of protection of the present invention.

Claims

1. A multi-objective hierarchical rolling optimization scheduling method for off-grid wind-solar green hydrogen production, characterized in that: The following steps are involved: (1) Establishing a total power balance model for an off-grid wind-solar hydrogen production scenario, wherein the total power balance model is used to describe the off-grid wind-solar hydrogen production scenario; (2) Establish a dynamic model of the energy storage device and introduce variables to describe the independent charging and discharging models into a unified mathematical model; (3) Based on the actual requirements of wind-solar hydrogen production scenarios, set physical constraints for the decision variables of the energy storage devices involved in the dynamic model; (4) Based on the actual needs of wind and solar hydrogen production scenarios, set the goals of coverage level optimization propositions, including minimum power abandonment, maximum hydrogen production and high energy utilization, and set priorities for the optimization goals; (5) Read the real-time wind and solar power generation data and the future multi-step power forecast data provided by the upstream through the industrial communication protocol; (6) Based on the data read in step (5), the optimization proposition set in step (4), the model in step (2), and the physical constraints of each decision variable in step (3), a mixed 0-1 integer nonlinear programming algorithm is used to perform optimization and solution. The optimized scheduling instruction obtained contains multiple beats, but only the first beat will be executed, and the optimal scheduling decision instruction for the first beat is obtained; (7) Execute the first optimization scheduling instruction in step (6), and in the next optimization cycle, re-optimize and solve according to the latest hydrogen production feedback data to achieve continuous optimization.

2. The multi-objective hierarchical rolling optimization scheduling method for off-grid wind-solar green hydrogen production according to claim 1 is characterized in that: The total power balance model in step (1) is used to describe the off-grid wind-solar hydrogen production scenario, specifically including the wind-solar power generation power, the charge and discharge power of the energy storage device, the electrolyzer power, the abandoned power and the charge and discharge status of the energy storage device.

3. The multi-objective hierarchical rolling optimization scheduling method for off-grid wind-solar green hydrogen production according to claim 1 is characterized in that: The step (1) specifically includes the following sub-steps: (1.1) Establish a total power balance model for the off-grid wind-solar hydrogen production system, incorporating wind-solar power generation, energy storage system charge and discharge power and status, electrolyzer power, and curtailed power into the model to fully describe the interactions between various power nodes; (1.2) By setting power flow conditions under different operating conditions, a dynamic power balancing mechanism covering wind and solar power generation, energy storage, and hydrogen production systems is formed, so that the system meets the balance conditions of power input and consumption at all times.

4. The multi-objective hierarchical rolling optimization scheduling method for off-grid wind-solar green hydrogen production according to claim 1 is characterized in that: The step (2) is specifically as follows: (2.1) Establish a dynamic charging model and a dynamic discharging model for the energy storage device. The dynamic charging model and the dynamic discharging model of the energy storage device can simultaneously describe the dynamic charging and discharging behaviors by introducing discrete 0 and 1 integer state variables; (2.2) By switching the state variables of the energy storage device, the energy storage device can seamlessly switch between charging and discharging behaviors, while ensuring that the dynamic model can describe the changing path of the system's charging and discharging behavior in a unified mathematical model.

5. The multi-objective hierarchical rolling optimization scheduling method for off-grid wind-solar green hydrogen production according to claim 1 is characterized in that: The constraints in step (3) are physical safety constraints in actual scenarios. They are based on the physical characteristics of the energy storage system and the electrolyzer equipment, and then set key constraints. They are hard constraints that cannot be relaxed. The decision variables covering the energy storage device include the upper and lower limits of the state of charge, the charge and discharge rate limit, the electrolyzer input power limit, and the maximum power change allowed for the electrolyzer within the safety range. These constraints are all incorporated into the scheduling model as inequality conditions to ensure that the scheduling optimization operates within the safety boundary.

6. The multi-objective hierarchical rolling optimization scheduling method for off-grid wind-solar green hydrogen production according to claim 1 is characterized in that: The step (4) specifically includes the following sub-steps: (4.1) Based on power balance and physical constraints, a multi-objective hierarchical optimization model is constructed with the core objectives of minimizing curtailed power, maximizing hydrogen production, and achieving high energy utilization; (4.2) By assigning weights to each optimization objective and setting a hierarchical optimization structure, the multi-objective model can achieve hierarchical allocation of priorities to meet the differentiated needs of scheduling objectives.

7. The multi-objective hierarchical rolling optimization scheduling method for off-grid wind-solar green hydrogen production according to claim 1 is characterized in that: The step (5) is specifically as follows: collecting the current power data of wind and solar power generation in real time through the industrial communication protocol interface, and combining it with the power generation forecast information of multiple time steps in the future as the input data of the rolling optimization; the real-time data and the forecast data are used as the time series variables of the model, so that the system can schedule the energy storage charging and discharging and the electrolyzer input power in advance according to the change trend of the power generation.

8. The multi-objective hierarchical rolling optimization scheduling method for off-grid wind-solar green hydrogen production according to claim 1 is characterized in that: The step (6) is specifically as follows: using a mixed 0-1 integer nonlinear programming algorithm to solve the multi-objective optimization model. Since the nonlinear optimization proposition involves 0 and 1 integer variables, it is necessary to use a mixed integer nonlinear programming algorithm to solve it. The solved optimization scheduling instruction contains multiple beats, but only the first beat will be executed; the algorithm also considers the power changes and constraints at future moments, adopts a step-by-step solution method, and optimizes the objective function value in each time step according to the input power data, thereby ensuring the best match between the current scheduling decision and the real-time data.

9. The multi-objective hierarchical rolling optimization scheduling method for off-grid wind-solar green hydrogen production according to claim 1 is characterized in that: Specifically, the step (7) is as follows: in a new optimization cycle, the optimization algorithm needs to re-solve the mixed 0-1 integer nonlinear programming problem based on the latest wind and solar power data, energy storage device status, power, and electrolyzer real-time operating power information, and still only execute the scheduling optimization instruction of the first beat; that is, in each rolling cycle, the optimal scheduling instruction of the first time step of the current cycle is executed; by executing the first scheduling instruction, the equipment operation instructions such as the energy storage charging and discharging power and the electrolyzer input power are adjusted, and at the same time, the latest data is re-collected in the next optimization cycle and the solution steps are repeated, so as to adjust the scheduling plan according to the latest system status and data; through rolling optimization, dynamic adjustment of the scheduling plan is achieved, so that the system scheduling can respond to real-time data changes in a timely manner and achieve continuous optimization.

10. A multi-objective hierarchical rolling optimization scheduling system for off-grid wind and solar green hydrogen production, characterized by: The following units are included: Establishing a total power balance model unit: establishing a total power balance model for an off-grid wind-solar hydrogen production scenario, wherein the total power balance model is used to describe the off-grid wind-solar hydrogen production scenario; Establish dynamic and mathematical model units: Establish a dynamic model of the energy storage device and introduce variables to describe the independent charging and discharging models into a unified mathematical model; Setting physical constraint units: Based on the actual requirements of wind-solar hydrogen production scenarios, set physical constraints for the decision variables of the energy storage devices involved in the dynamic model; Setting optimization proposition unit: Based on the actual needs of wind and solar hydrogen production scenarios, set the goals of coverage-level optimization propositions, including minimum power curtailment, maximum hydrogen production, and high energy utilization, and set priorities for the optimization goals; Power prediction unit: reads real-time wind and solar power generation data and future multi-step power prediction data provided by upstream through industrial communication protocols; Optimization solving unit: Based on the data read by the power prediction unit, the optimization proposition set in the optimization proposition unit, the model established in the dynamic and mathematical model units, and the physical constraints of each decision variable in the physical constraint unit, the optimization solution is performed using a mixed 0-1 integer nonlinear programming algorithm. The solved optimization scheduling instruction contains multiple beats, but only the first beat will be executed to obtain the optimal scheduling decision instruction for the first beat; Continuous optimization unit: Executes the first-shot optimization scheduling instruction in the optimization solution unit, and in the next optimization cycle, re-optimizes and solves according to the latest hydrogen production feedback data to achieve continuous optimization.

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