Method and system for dynamic power distribution and efficiency optimization of hydrogen production system

Abstract

The invention discloses a method and system for dynamic power distribution and efficiency optimization of a hydrogen production system, and belongs to the technical field of power distribution of a power grid, and the method comprises the steps: obtaining a current total power demand instruction, the real-time state information of each electrolytic cell module, the state information of a hydrogen storage system, and the power fluctuation prediction information of renewable energy sources; power grid real-time state information is formed; real-time efficiency values of all the electrolytic cell modules at different power points are determined based on an electrolytic cell module efficiency model library; the overall hydrogen production efficiency maximization of the power grid system is used as a target, and the optimal power distribution scheme of each electrolytic cell module is calculated through an optimization algorithm; issuing a power instruction to each electrolytic cell module; and continuously monitoring the operation state of the monitoring power grid system, and updating the efficiency model library of the electrolytic cell module and the real-time state information of the power grid in real time. The energy consumption is reduced, the operation time of the module in a low-efficiency area is shortened, the operation time of the module under an adverse working condition is shortened, the service life of the system is prolonged, and the reliability of the system is improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power distribution of power grid, and particularly relates to a method and system for dynamic power distribution and efficiency optimization of a hydrogen production system. BACKGROUND

[0002] Using renewable energy (such as wind power and photovoltaic power) to electrolyze water to produce hydrogen is an important way to achieve large-scale production and meets the development needs of a low-carbon energy system under the carbon neutralization goal. However, renewable energy output has significant intermittency, volatility and uncertainty, which leads to challenges for a hydrogen production system directly coupled with an electrolyzer. When the electrolyzer is operated at a low load or the load changes dramatically, the efficiency of the electrolyzer will decrease significantly and may affect the service life of the electrolyzer. Therefore, how to maintain the high efficiency, stable and safe operation of the hydrogen production system in a wide power fluctuation range has become one of the core technical problems to be solved in the field of renewable energy hydrogen production.

[0003] In order to cope with the fluctuations of renewable energy and improve the adaptability of the system in a wide power range, a common solution is to use multiple electrolyzer modules in combination. The previous methods for power distribution and efficiency optimization of the hydrogen production system focused on simple control strategies, such as sequentially starting or stopping electrolyzer modules in a preset sequence. Or the strategy of fixed power distribution ratio, to a certain extent, realizes the distribution of power, but fails to deeply consider and utilize different types of electrolyzers. Alkaline electrolyzers may have high efficiency in the medium to high load range, but their efficiency drops sharply in the low load range, which makes the system unable to work in the optimal efficiency combination state at varying power points.

[0004] In the power allocation of the existing hydrogen production system, a single type of electrolyzer cannot maintain high efficiency in a wide power range (such as 10%-120% of the rated power), and the efficiency is difficult to remain stable when dealing with large fluctuations in power. The existing combined system cannot make fine adaptive adjustments according to the real-time changing power demand and the dynamic efficiency characteristics of each module. When dealing with fluctuations in energy power, the electrolyzer modules are frequently started and stopped or repeatedly pass through the low-efficiency operating range, which not only causes large fluctuations in the overall efficiency of the system, but also increases the energy consumption of hydrogen production, thereby affecting the service life and reliability of the electrolyzer. In summary, in the current hydrogen production system, the efficiency is difficult to remain stable when dealing with large fluctuations in power, and fine adaptive adjustments cannot be made according to the real-time power and module efficiency characteristics. When responding to power fluctuations, the system may be frequently started and stopped, leading to large fluctuations in system efficiency, increasing the energy consumption of hydrogen production, and affecting the service life and reliability of the electrolyzer. SUMMARY

[0005] The application provides a hydrogen production system dynamic power distribution and efficiency optimization method and system, aiming to solve the problem that the current hydrogen production system cannot maintain stable efficiency when dealing with large power fluctuations, cannot perform fine adaptive adjustment according to real-time power and module efficiency characteristics, and may frequently start and stop when corresponding to power fluctuations, resulting in large system efficiency fluctuations, increased hydrogen production energy consumption, and affected electrolytic cell operation life and reliability.

[0006] To achieve the above-mentioned purpose, the application adopts the following technical solutions: The application provides a hydrogen production system dynamic power distribution and efficiency optimization method, comprising the following steps: S1, real-time monitoring of the operating state of the power grid system, obtaining the current total power demand instruction, real-time state information of each electrolytic cell module, hydrogen storage system state information, and renewable energy power fluctuation prediction information, forming power grid real-time state information; S2, based on the pre-established electrolytic cell module efficiency model library, determining the real-time efficiency values of each electrolytic cell module in the electrolytic cell module efficiency model library at different power points; The electrolytic cell module efficiency model library includes efficiency and power characteristic curves of at least two different types of electrolytic cell modules under different working conditions; S3, maximizing the overall hydrogen production efficiency of the power grid system as the target, combining the power grid real-time state information and the real-time efficiency values of each electrolytic cell module at different power points, and calculating the optimal power distribution scheme of each electrolytic cell module through an optimization algorithm; S4, according to the optimal power distribution scheme of each electrolytic cell module, issuing power instructions to each electrolytic cell module for power distribution; S5, continuously monitoring the operating state of the monitored power grid system, real-time updating the electrolytic cell module efficiency model library and the power grid real-time state information, and repeatedly executing S1 to S4 to realize dynamic power distribution and efficiency optimization of the hydrogen production system.

[0007] In some embodiments, in S1, the power grid real-time state information includes the availability, temperature, pressure, power upper and lower limits, and ramp rate limit of each electrolytic cell module.

[0008] In some embodiments, in S2, the electrolytic cell module efficiency model library is formed by experimental data and historical operation data and is periodically updated.

[0009] In some embodiments, in S2, the electrolytic cell module includes at least two types of alkaline electrolytic cells and proton exchange membrane electrolytic cells.

[0010] In some embodiments, in S3, the optimization algorithm used to calculate the optimal power distribution scheme of each electrolytic cell module includes a linear programming or nonlinear programming algorithm.

[0011] Further, in S3, the optimization algorithm for calculating the optimal power distribution scheme of each electrolyzer module also combines power fluctuation prediction information for forward-looking power distribution.

[0012] In some embodiments, S3 also includes considering the influence of the thermal inertia and dynamic response characteristics of each electrolyzer module on power distribution.

[0013] In some embodiments, in S4, the power instructions issued to each electrolyzer module need to meet the safety operation boundary constraints of each electrolyzer module.

[0014] In some embodiments, in S5, continuously monitoring the operating state of the power grid system includes real-time evaluation of hydrogen production efficiency, energy consumption, and module health status.

[0015] The present application also provides a hydrogen production system dynamic power distribution and efficiency optimization system, which is used to implement the above-mentioned hydrogen production system dynamic power distribution and efficiency optimization method. The system includes a state information acquisition module, a real-time efficiency value determination module, an optimal power distribution module, an issued and executed power distribution module, and a monitoring and updating module, wherein: The state information acquisition module is used to monitor the operating state of the power grid system in real time, obtain the current total power demand instruction, the real-time state information of each electrolyzer module, the state information of the hydrogen storage system, and the renewable energy power fluctuation prediction information, and form the power grid real-time state information. The real-time efficiency value determination module is used to determine the real-time efficiency value of each electrolyzer module at different power points in the electrolyzer module efficiency model library based on the pre-established electrolyzer module efficiency model library. The electrolyzer module efficiency model library includes efficiency and power characteristic curves of at least two different types of electrolyzer modules under different working conditions. The optimal power distribution module is used to maximize the overall hydrogen production efficiency of the power grid system as the target, combine the power grid real-time state information and the real-time efficiency value of each electrolyzer module at different power points, and calculate the optimal power distribution scheme of each electrolyzer module through an optimization algorithm. The issued and executed power distribution module is used to issue power instructions to each electrolyzer module according to the optimal power distribution scheme of each electrolyzer module, so as to execute power distribution. The monitoring and updating module is used to continuously monitor the operating state of the power grid system, update the electrolyzer module efficiency model library and the power grid real-time state information in real time, and repeatedly execute the above-mentioned modules to realize dynamic power distribution and efficiency optimization of the hydrogen production system.

[0016] Compared with the prior art, the hydrogen production system dynamic power distribution and efficiency optimization method and system of the present application has the following beneficial effects: This invention provides a method for dynamic power allocation and efficiency optimization in a hydrogen production system. By establishing a multi-dimensional system state, collecting the current total power command signal, and obtaining the real-time status of each electrolyzer module, the hydrogen storage system status, and power fluctuation prediction information, it provides a data foundation for subsequent optimization decisions. This allows for the mitigation of fluctuation impacts and prevents the system from frequently entering unfavorable operating conditions due to passive responses. The invention also includes a pre-established electrolyzer module efficiency model library containing efficiency and power characteristic curves of different types of electrolyzers under various operating conditions. This digitizes and models the static efficiency characteristics of different electrolyzers, overcoming traditional fixed strategies or simple start-stop strategies. Furthermore, based on the provided real-time data and efficiency models, online calculations are performed using optimization algorithms to improve the average hydrogen production efficiency over a wide power range and reduce energy consumption. The method can calculate in real-time how to decompose the power task to each module under the current total power demand to maximize the overall system efficiency. This invention updates the model and state information through continuous monitoring, enabling the entire system to be adaptive and self-learning. It adapts to the slow decline in electrolyzer performance, changes in environmental conditions, and the fluctuating characteristics of renewable energy, ensuring that the efficiency model always remains highly consistent with the actual state of the equipment and guaranteeing optimization accuracy during long-term operation. Attached Figure Description

[0017] The accompanying drawings are provided to further understand the invention and constitute a part of this invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0018] Figure 1 This is a schematic flowchart of a method for dynamic power allocation and efficiency optimization of a hydrogen production system according to the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0020] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0021] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0022] It should be noted that the apparatus and methods disclosed in the embodiments herein can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings show the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments herein. In this regard, each block in a flowchart or block diagram may represent a module, program, or part of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system to perform the specified function or action, or can be implemented using a combination of dedicated hardware and computer instructions.

[0023] In addition, the functional modules in the various embodiments of this article can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0024] like Figure 1 As shown, the present invention provides a method for dynamic power allocation and efficiency optimization of a hydrogen production system, comprising the following steps: S1. Monitor the operating status of the power grid system in real time, obtain the current total power demand command, the real-time status information of each electrolyzer module, the status information of the hydrogen storage system, and the power fluctuation prediction information of renewable energy, and form real-time power grid status information; S2. Based on the pre-established electrolytic cell module efficiency model library, determine the real-time efficiency value of each electrolytic cell module in the electrolytic cell module efficiency model library at different power points; Among them, the electrolytic cell module efficiency model library includes efficiency and power characteristic curves of at least two different types of electrolytic cell modules under different operating conditions; S3. Taking the maximization of the overall hydrogen production efficiency of the power grid system as the goal, and combining the real-time status information of the power grid and the real-time efficiency values ​​of each electrolyzer module at different power points, the optimal power allocation scheme of each electrolyzer module is calculated through optimization algorithms. S4. Based on the optimal power allocation scheme of each electrolytic cell module, issue power commands to each electrolytic cell module in order to execute power allocation; S5. Continuously monitor the operating status of the power grid system, update the efficiency model library of the electrolyzer module and the real-time status information of the power grid in real time, and repeat S1 to S4 to realize dynamic power allocation and efficiency optimization of the hydrogen production system.

[0025] The method of this invention aims to optimize the overall hydrogen production efficiency of the system. It employs an optimization algorithm to consider the power fluctuation trend and implement a forward-looking power allocation strategy. It also considers the influence mechanism of the electrolyzer's thermal inertia and dynamic response characteristics on power allocation decisions. By establishing and applying a real-time efficiency model library for electrolyzer modules for online optimization, this invention significantly enhances the adaptability of the hydrogen production system to drastically fluctuating power sources, providing technical support for large-scale hydrogen production from renewable energy. By avoiding the operation of the electrolyzer in inefficient or dangerous areas such as low load, it reduces frequent start-ups and shutdowns and drastic load changes, effectively extending the service life of equipment such as the electrolyzer, reducing maintenance costs, and improving the economy and reliability of the entire system.

[0026] In some embodiments, the real-time power grid status information of the present invention includes the availability, temperature, pressure, power upper and lower limits, and ramp rate limits of each electrolyzer module. When seeking the optimal efficiency solution, the optimization algorithm must treat the physical constraints and safety boundaries of the equipment as hard conditions. This ensures that tasks are not assigned to faulty or under maintenance modules; temperature and pressure are key parameters reflecting the real-time operating status of the electrolyzer and ensuring its operation within a safe window, avoiding the risk of overheating and overpressure; power upper and lower limits define the operable range of each module, preventing the algorithm from issuing instructions beyond the equipment's capabilities; ramp rate limits ensure the smoothness of power regulation, avoiding mechanical or thermal stress shocks to the electrolyzer due to rapid changes in power commands, thus extending the service life of the equipment.

[0027] The electrolyzer module efficiency model library of this invention is established and regularly updated using experimental data and historical operating data, ensuring the accuracy and timeliness of the efficiency model and thus guaranteeing the optimization effect under long-term operation. The efficiency characteristics of an electrolyzer drift slowly with equipment aging, changes in catalyst activity, and alterations in the operating environment. A static model established solely based on initial experimental data will inevitably show deviations in the long run, leading to the failure of optimization decisions. By periodically updating the model by integrating historical operating data, the actual changes in equipment performance can be dynamically tracked and fitted, ensuring that the model always remains consistent with reality and guaranteeing the long-term accuracy and reliability of power allocation optimization decisions.

[0028] Based on the above, the electrolyzer module of the present invention includes at least two types of alkaline electrolyzers and proton exchange membrane (PEM) electrolyzers. Alkaline electrolyzers generally have the advantages of low cost and long lifespan, but their load response range is narrow and their efficiency is low at low loads; while PEM electrolyzers have the advantages of wide load response range, fast response speed, and high partial load efficiency, but their cost is higher. By combining the two, the optimization algorithm can fully leverage the advantages of PEM electrolyzers in low-load and rapidly fluctuating scenarios, while allowing alkaline electrolyzers to operate more in their efficient medium-to-high load range, achieving higher system efficiency over a wider power fluctuation range.

[0029] Furthermore, the optimization algorithm of the present invention includes linear programming or nonlinear programming algorithms. By modeling the power allocation problem as a constrained optimization problem, linear programming or nonlinear programming can guarantee that, under given constraints, a globally or locally optimal power allocation scheme can be found quickly and accurately.

[0030] Based on this, as an optional approach, the optimization algorithm of this invention also incorporates power fluctuation prediction information for forward-looking power allocation, transforming passive response into active forward-looking control. By introducing prediction information, the algorithm is allowed to predict power change trends in the near future. If a sharp drop in power is predicted, the algorithm can instruct the alkaline electrolyzer, which has a slower response, to begin a smooth load reduction in advance, preventing it from entering an inefficient zone or affecting its lifespan due to a sharp load reduction. At the same time, the load of the PEM electrolyzer can be adjusted in advance to prepare for fluctuations, further improving the overall efficiency of the system under fluctuating conditions.

[0031] Furthermore, this invention considers the impact of the thermal inertia and dynamic response characteristics of each electrolytic cell module on power allocation, making the optimization decision more closely aligned with physical reality and improving performance under dynamic processes. The efficiency and safety of an electrolytic cell are closely related to its thermal state, and its temperature changes exhibit thermal inertia, meaning that changes in power commands cannot instantly trigger a temperature response. Similarly, different electrolytic cells have different power response speeds. Considering thermal inertia can avoid thermal stress shocks and overheating risks caused by excessively rapid changes in power commands; considering dynamic response characteristics ensures that the allocated power change rate is achievable by each module, preventing a disconnect between commands and actual execution.

[0032] This invention limits the power commands issued to meet the safe operating boundary constraints of each electrolytic cell module, setting insurmountable preset values ​​for the entire optimization control process and ensuring reliable system operation. These safe operating boundary constraints include maximum or minimum operating pressure, maximum or minimum temperature, maximum allowable current density, and safe gas purity ranges. This deeply embeds safety concepts into the automated decision-making logic, achieving a synergistic design of safety and efficiency, and avoiding the potential risk of sacrificing safety for efficiency.

[0033] The continuous monitoring of this invention includes real-time assessment of hydrogen production efficiency, energy consumption, and module health status, thereby enabling the management of system performance and equipment status. The real-time assessment of hydrogen production efficiency and energy consumption can be used to verify the actual effect of optimized control and provide feedback data for model updates. The real-time assessment of module health status enables predictive maintenance, providing early warnings before severe performance degradation or failure, avoiding unplanned downtime, and further improving the availability and reliability of the system.

[0034] This invention also provides a system for dynamic power allocation and efficiency optimization of a hydrogen production system. This system is used to implement the aforementioned method for dynamic power allocation and efficiency optimization of a hydrogen production system. The system includes a status information acquisition module, a real-time efficiency value determination module, an optimal power allocation module, a power allocation execution module, and a monitoring and update module, wherein: Status information acquisition module: used to monitor the operating status of the power grid system in real time, acquire the current total power demand command, the real-time status information of each electrolyzer module, the status information of the hydrogen storage system, and the renewable energy power fluctuation prediction information, and form real-time power grid status information; Real-time efficiency value determination module: Based on a pre-established electrolyzer module efficiency model library, it determines the real-time efficiency value of each electrolyzer module at different power points in the electrolyzer module efficiency model library. Among them, the electrolytic cell module efficiency model library includes efficiency and power characteristic curves of at least two different types of electrolytic cell modules under different operating conditions; Optimal power allocation module: It aims to maximize the overall hydrogen production efficiency of the power grid system. Combining real-time power grid status information and the real-time efficiency values ​​of each electrolyzer module at different power points, it calculates the optimal power allocation scheme for each electrolyzer module through optimization algorithms. Power allocation module: Used to issue power commands to each electrolytic cell module according to the optimal power allocation scheme of each electrolytic cell module, so as to execute power allocation; Monitoring and Update Module: This module continuously monitors the operating status of the power grid system, updates the efficiency model library of the electrolyzer module and the real-time status information of the power grid in real time, and repeatedly executes the above module to achieve dynamic power allocation and efficiency optimization of the hydrogen production system.

[0035] In some practical applications, the system configuration of this invention includes a combined hydrogen production system and a dynamic efficiency model library. The combined hydrogen production system comprises at least two electrolyzer modules with different efficiency and power characteristic curves. For example, the first module may have high efficiency at low loads but a low power ceiling, while the second module may have high efficiency near its rated power but poor efficiency at low loads. The dynamic efficiency model library is used to establish or store real-time efficiency models for each electrolyzer module under different power, temperature, and pressure conditions.

[0036] This invention aims to maximize the overall hydrogen production efficiency or minimize the energy consumption per unit of hydrogen production while meeting the current total power demand, thereby achieving the goal of dynamic power allocation optimization. The model input may include, but is not limited to: the current total power command, the real-time efficiency models of each module, module status such as availability, temperature, and pressure, hydrogen storage status affecting full production capacity, and fluctuation prediction information. The model constraints may include, but are not limited to, the upper and lower limits of power for each module, ramp-up rate limits, and safe operating boundaries.

[0037] In summary, the present invention provides a method and system for dynamic power allocation and efficiency optimization in a hydrogen production system. Through dynamic optimization allocation, the system operates at the most efficient combination of power points within a wide power range, reducing the average energy consumption for hydrogen production. By combining fluctuation prediction with forward-looking allocation, the operating time of modules in inefficient regions is reduced, and the impact of fluctuations on efficiency and lifespan is mitigated. Optimized allocation reduces the operating time of modules under adverse operating conditions, thereby improving the overall reliability of the system and demonstrating significant practical value.

[0038] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Anyone skilled in the art can readily implement the present invention according to the description and above. Any modifications, alterations, or equivalent variations made using the technical content disclosed above are equivalent embodiments of the present invention. Furthermore, any modifications, alterations, or variations made to the above embodiments based on the essential technology of the present invention are still within the protection scope of the present invention.

Claims

1. A method for dynamic power allocation and efficiency optimization of a hydrogen production system, characterized in that, Includes the following steps: S1. Monitor the operating status of the power grid system in real time, obtain the current total power demand command, the real-time status information of each electrolyzer module, the status information of the hydrogen storage system, and the power fluctuation prediction information of renewable energy, and form real-time power grid status information; S2. Based on the pre-established electrolytic cell module efficiency model library, determine the real-time efficiency value of each electrolytic cell module in the electrolytic cell module efficiency model library at different power points; Among them, the electrolytic cell module efficiency model library includes efficiency and power characteristic curves of at least two different types of electrolytic cell modules under different operating conditions; S3. Taking the maximization of the overall hydrogen production efficiency of the power grid system as the goal, and combining the real-time status information of the power grid and the real-time efficiency values ​​of each electrolyzer module at different power points, the optimal power allocation scheme of each electrolyzer module is calculated through optimization algorithms. S4. Based on the optimal power allocation scheme of each electrolytic cell module, issue power commands to each electrolytic cell module in order to execute power allocation; S5. Continuously monitor the operating status of the power grid system, update the efficiency model library of the electrolyzer module and the real-time status information of the power grid in real time, and repeat S1 to S4 to realize dynamic power allocation and efficiency optimization of the hydrogen production system.

2. The method for dynamic power allocation and efficiency optimization of a hydrogen production system according to claim 1, characterized in that, In S1, the real-time status information of the power grid includes the availability, temperature, pressure, upper and lower power limits, and ramp rate limits of each electrolytic cell module.

3. The method for dynamic power allocation and efficiency optimization of a hydrogen production system according to claim 1, characterized in that, In S2, the efficiency model library of the electrolytic cell module is established and updated regularly through experimental data and historical operating data.

4. The method for dynamic power allocation and efficiency optimization of a hydrogen production system according to claim 1, characterized in that, In S2, the electrolyzer module includes at least two types of alkaline electrolyzers and proton exchange membrane electrolyzers.

5. The method for dynamic power allocation and efficiency optimization of a hydrogen production system according to claim 1, characterized in that, In step S3, the optimization algorithm used to calculate the optimal power allocation scheme for each electrolytic cell module includes linear programming or nonlinear programming algorithms.

6. The method for dynamic power allocation and efficiency optimization of a hydrogen production system according to claim 5, characterized in that, In S3, the optimization algorithm for calculating the optimal power allocation scheme for each electrolytic cell module also incorporates power fluctuation prediction information for forward-looking power allocation.

7. The method for dynamic power allocation and efficiency optimization of a hydrogen production system according to claim 1, characterized in that, S3 further includes considering the impact of the thermal inertia and dynamic response characteristics of each electrolytic cell module on power distribution.

8. The method for dynamic power allocation and efficiency optimization of a hydrogen production system according to claim 1, characterized in that, In step S4, when issuing power commands to each electrolytic cell module, the safety operation boundary constraints of each electrolytic cell module must be met.

9. The method for dynamic power allocation and efficiency optimization of a hydrogen production system according to claim 1, characterized in that, In S5, the operating status of the power grid system is continuously monitored, including real-time assessment of hydrogen production efficiency, energy consumption, and module health status.

10. A system for dynamic power allocation and efficiency optimization in a hydrogen production system, characterized in that, The system is used to implement the method for dynamic power allocation and efficiency optimization of the hydrogen production system according to any one of claims 1-9. The system includes a status information acquisition module, a real-time efficiency value determination module, an optimal power allocation module, a power allocation execution module, and a monitoring and update module, wherein: Status information acquisition module: used to monitor the operating status of the power grid system in real time, acquire the current total power demand command, the real-time status information of each electrolyzer module, the status information of the hydrogen storage system, and the renewable energy power fluctuation prediction information, and form real-time power grid status information; Real-time efficiency value determination module: Based on a pre-established electrolyzer module efficiency model library, it determines the real-time efficiency value of each electrolyzer module at different power points in the electrolyzer module efficiency model library. Among them, the electrolytic cell module efficiency model library includes efficiency and power characteristic curves of at least two different types of electrolytic cell modules under different operating conditions; Optimal power allocation module: It aims to maximize the overall hydrogen production efficiency of the power grid system. Combining real-time power grid status information and the real-time efficiency values ​​of each electrolyzer module at different power points, it calculates the optimal power allocation scheme for each electrolyzer module through optimization algorithms. Power allocation module: Used to issue power commands to each electrolytic cell module according to the optimal power allocation scheme of each electrolytic cell module, so as to execute power allocation; Monitoring and Update Module: This module continuously monitors the operating status of the power grid system, updates the efficiency model library of the electrolyzer module and the real-time status information of the power grid in real time, and repeatedly executes the above module to achieve dynamic power allocation and efficiency optimization of the hydrogen production system.

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