Optimization Configuration Methods for New Energy Hydrogen Production Systems

Abstract

This application relates to the energy field and discloses a method for optimizing the configuration of a new energy hydrogen production system. The method includes: acquiring reference wind power data, photovoltaic power data, and initial system configuration parameters for a target area; based on the power data and initial configuration parameters, cyclically simulating the operating state of the hydrogen supply system within an hourly cycle, dynamically calculating hourly simulation results, which include multiple dynamic operating parameters of the new energy hydrogen production system; the operating state of the new energy hydrogen production system at the next moment will be simulated using the dynamic operating parameters of the new energy hydrogen production system at the previous moment as the original input; and dynamically adjusting and optimizing the energy allocation of the new energy hydrogen production system based on the hourly simulation results. This application, through an innovative optimization configuration algorithm, improves the operating efficiency and stability of the new energy hydrogen production system, achieving globally optimal configuration during the system design phase.

Description

Technical Field

[0001] This application relates to the energy field, and in particular to an optimized configuration method for a new energy hydrogen production system. Background Technology

[0002] This section is intended to provide background or context for the embodiments of this application as set forth in the claims. The content in this section is for reference only and does not constitute an admission or confirmation that it is prior art that has been disclosed.

[0003] Against the backdrop of increasing global fossil fuel consumption and escalating environmental pollution, the development and utilization of new energy sources have become a core issue in the global energy transition. Hydrogen energy, as a clean, efficient, and storable energy carrier, is widely considered a key component of the future energy structure. Hydrogen production methods are diverse, among which the production of green hydrogen using renewable energy sources such as wind and solar power through water electrolysis technology is gradually gaining global attention due to its environmental friendliness and sustainability.

[0004] However, the intermittency and instability of wind and solar energy pose significant challenges to the continuous and stable operation of hydrogen production systems. To achieve stable hydrogen supply, precise configuration and dynamic scheduling of the hydrogen production system are essential. However, traditional hydrogen production system configuration methods often rely on experience or simple optimization algorithms, which fail to achieve ideal optimization results when dealing with large-scale, complex system configurations. Therefore, developing efficient optimization algorithms to achieve optimal configuration of new energy hydrogen production systems has become an urgent technical challenge.

[0005] Currently, the academic community has made some progress in the research of the operating logic of hydrogen production systems, with researchers dedicated to exploring the interactions between various components and their influencing factors. The core objective is to maximize energy utilization within a given configuration scale by optimizing system operation strategies. Some research focuses on constructing mathematical programming models to solve the optimal configuration problem of hydrogen production systems using precise mathematical models. These models encompass various programming methods, including linear programming, nonlinear programming, and mixed-integer programming, seeking the optimal system configuration under given system parameters and operational constraints by defining objective functions and constraints. However, although these mathematical programming methods can theoretically provide globally optimal solutions, practical applications face challenges in computational efficiency and practical feasibility due to the complexity and dynamics of the systems.

[0006] While many scholars have addressed and optimized energy efficiency in the study of hydrogen production system operation logic, a comprehensive and effective solution for optimizing system configuration in the initial production phase remains elusive. In other words, although these methods can guide system operation at certain stages, achieving globally optimal configuration during system design remains a largely unresolved challenge. In real-world production environments, the time-varying nature of wind and solar energy, along with the system's dependence on its previous state, makes it difficult for traditional mathematical expressions to accurately describe these complex optimization objectives.

[0007] Furthermore, most existing optimization algorithms employ a single-threaded computation model, failing to fully utilize the parallel computing capabilities of modern multi-core CPUs, which limits performance improvement. Therefore, improving the accuracy and computational efficiency of hydrogen production system optimization configuration based on advanced computing technologies and algorithms has become a crucial technology that urgently needs breakthrough. Summary of the Invention

[0008] The purpose of this application is to provide an optimized configuration method for a new energy hydrogen production system. Through an innovative optimized configuration algorithm, the operating efficiency and stability of the new energy hydrogen production system are improved, and the globally optimal configuration is achieved in the system design stage.

[0009] This application discloses an optimized configuration method for a new energy hydrogen production system, including:

[0010] Acquire reference wind power data, photovoltaic power data, and initial system configuration parameters for the target area;

[0011] Based on the power data and initial configuration parameters, the operating status of the hydrogen supply system is simulated hourly within the cycle, and the hourly simulation results are dynamically calculated. The hourly simulation results include multiple dynamic operating parameters of the new energy hydrogen production system. The operating status of the new energy hydrogen production system at the next moment will be simulated using the dynamic operating parameters of the new energy hydrogen production system at the previous moment as the original input.

[0012] The energy destination of the new energy hydrogen production system is dynamically adjusted and optimized based on the hourly simulation results.

[0013] In a preferred embodiment, the initial configuration parameters include: wind power scale, photovoltaic scale, energy storage system capacity, initial energy storage system capacity, hydrogen storage system capacity, initial hydrogen storage system capacity, and hydrogen production scale.

[0014] The dynamic calculation to obtain the hourly simulation results further includes:

[0015] Based on the user-input standard annual reference power, the photovoltaic scale, and the wind power scale, obtain the actual photovoltaic power and actual wind power at time i:

[0016]

[0017] Among them, pv i Let ref_pv be the actual photovoltaic power at time i. i Let ref_pv_scale be the reference photovoltaic power at time i, ref_pv_scale be the reference photovoltaic scale, select_pv_scale be the selected photovoltaic scale, and wind be the reference photovoltaic power at time i. i Let ref_wind be the actual wind power at time i. i Ref_wind_scale represents the reference wind power at time i, ref_wind_scale represents the reference wind power scale, and select_wind_scale represents the selected wind power scale.

[0018] In a preferred embodiment, it further includes:

[0019] Based on the obtained real-time photovoltaic power and real-time wind power, the potential output of combined wind and solar hydrogen production at time i is estimated, where:

[0020] If the hydrogen storage tank had reached its maximum capacity at the previous moment... The combined wind and solar hydrogen production at time i is calculated as follows:

[0021]

[0022] Where unit_power is the unit power consumption of the hydrogen production system, and target_H i The optimized target hydrogen supply at time i is limited by the rated hydrogen supply of downstream demand. The hydrogen production at time i is determined by the smaller of two factors: the rated hydrogen supply of downstream demand and the hydrogen production that may be generated when all photovoltaic and wind power resources are used for hydrogen production.

[0023] If the hydrogen storage tank mentioned in the previous moment has not reached its maximum capacity. but

[0024] First, calculate the maximum hydrogen capacity that can be stored in the hydrogen storage tank at time i.

[0025]

[0026] Where select_storage_H is the selected system hydrogen storage tank capacity. This represents the remaining hydrogen storage capacity in the hydrogen storage tank at the previous moment.

[0027] Then calculate the combined wind and solar hydrogen production at time i.

[0028]

[0029] Among them, prod_H max The selected maximum hydrogen production scale;

[0030] The combined wind and solar hydrogen production at time i is affected by three key factors: the amount of hydrogen produced when all wind and solar resources are used for hydrogen production, the pre-selected hydrogen production scale (i.e., the maximum hydrogen production capacity of the hydrogen production system per unit time), and the remaining storage capacity of the hydrogen storage tank.

[0031] In a preferred embodiment, it further includes:

[0032] Based on the obtained combined wind and solar hydrogen production at time i, calculate the hydrogen supply gap at time i:

[0033] short_H i =max(target_H i -prod_H i ,0);

[0034] And the remaining electricity generated after the combined wind and solar hydrogen production at time i:

[0035] left_P i =pv i +wind i -prod_H i *unit_power

[0036] If the remaining battery power is greater than 0, i.e., left_P i If the value is greater than 0, the remaining electricity is used to charge the energy storage system.

[0037] In a preferred embodiment, it further includes:

[0038] Calculate the remaining storage capacity of the energy storage system:

[0039]

[0040] Among them, avail_E i Let i be the maximum charging capacity of the energy storage system at time i. For the selected system energy storage capacity, The remaining power of the energy storage device at the previous moment;

[0041] Based on the remaining storage capacity of the energy storage system, calculate the actual charging amount of the energy storage system at time i:

[0042] charge_E i =min(left_P) i *η charge ,avail_E i )

[0043] Where, η charg Improve the charging efficiency of energy storage systems;

[0044] If the energy storage system has remaining power after charging, the surplus power is considered for grid connection. The amount of surplus power grid connection at time i is:

[0045]

[0046] In a preferred embodiment, it further includes:

[0047] If there is a hydrogen supply gap at time i, short_H i >0 and the remaining hydrogen storage capacity in the hydrogen storage tank is lower than the set value left_H i-1 <select_H min If the new energy hydrogen production system starts energy storage discharge to produce hydrogen, the amount of electricity produced by the energy storage discharge will be determined by the smaller of the remaining electricity in the energy storage system and the electricity required to fill the hydrogen supply gap.

[0048]

[0049] Where, η output For the discharge efficiency of the energy storage system;

[0050] Based on the amount of hydrogen produced by the energy storage discharge, calculate the remaining energy of the energy storage system after time i:

[0051]

[0052] The actual hydrogen charge in the hydrogen storage tank at time i is obtained by adding the electricity generated from the hydrogen production through the energy storage discharge to the combined wind and solar power hydrogen production.

[0053]

[0054] Based on the optimized target hydrogen supply, the actual hydrogen filling amount of the hydrogen storage tank, and the remaining hydrogen storage amount of the hydrogen storage tank at the previous moment, the actual hydrogen supply at moment i is obtained:

[0055]

[0056] Based on the actual hydrogen supply, the remaining hydrogen in the hydrogen storage tank at time i is obtained as follows:

[0057] left_H i =left_H i-1 +input_H i -output_H i .

[0058] In a preferred embodiment, it further includes:

[0059] The hourly simulation results are smoothed by hydrogen supply processing. The target hydrogen supply is iteratively adjusted to reduce supply spikes and ensure supply continuity, generating a smoothed hydrogen demand curve, which includes:

[0060] Determine the minimum hydrogen supply required for stable system operation;

[0061] Set the target hydrogen supply starting from the rated hydrogen supply capacity and adjust it according to system requirements;

[0062] At any given moment, the minimum hydrogen supply is calculated based on the system's operating status;

[0063] If the minimum hourly hydrogen supply is lower than the set minimum hydrogen supply, the hydrogen supply will be adjusted by decreasing the target hydrogen supply by 5%.

[0064] Repeat the previous step until the minimum hydrogen supply per hour thereafter is no longer lower than the set minimum hydrogen supply, or the target hydrogen supply has been reduced to the minimum hydrogen supply.

[0065] In a preferred embodiment, it further includes:

[0066] The hourly simulation results are used to determine the optimization objective, which is to minimize resource consumption by allocating the proportions and scales of five configuration factors, namely photovoltaic scale, wind power scale, hydrogen production scale, energy storage capacity and hydrogen storage capacity, to meet downstream hydrogen supply demand.

[0067] Feasibility criteria are introduced, including the annual hydrogen supply utilization hours T_supply, the proportion of time when the annual operating hours are not less than the minimum hydrogen supply T_(supply_min), and the percentage of surplus electricity connected to the grid R_grid, to ensure that the optimized configuration scheme meets the hydrogen supply demand while reducing resource waste caused by over-configuration of electricity.

[0068] In a preferred embodiment, it further includes:

[0069] When calculating the operating status of the hydrogen supply system during the hourly cyclic simulation, an iterative method is used for global optimization, and a pruning strategy is introduced to accelerate the iteration process. Before performing production simulation calculations on a specific combination of system configuration parameters, it is first evaluated whether the resource consumption of the configuration scheme is lower than the resource consumption of the currently known optimal configuration scheme.

[0070] If the resource consumption of the configuration scheme is not less than that of the known optimal scheme, then the scheme is pruned directly, the production simulation process is skipped, and the next configuration scheme is calculated.

[0071] In a preferred embodiment, it further includes:

[0072] Based on the pruning strategy, a multi-threaded parallel computing algorithm is introduced. By setting a shared memory area and global parameters, each CPU core of the computer is allowed to start a thread and run a production simulation task with a different configuration scheme independently. A load balancing strategy is used to avoid CPU resource idleness and improve computing efficiency.

[0073] During parallel computing, concurrency control strategies are employed to ensure data consistency and thread safety. By setting different lock types and performing multiple feasible solution checks, the modified shared parameters are ensured to be the current globally optimal parameters, thus avoiding concurrency conflicts.

[0074] This application discloses a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the method described above.

[0075] This application also discloses a computer program product including computer-executable instructions that, when executed by a processor, implement the steps in the method described above.

[0076] In the embodiments of this application, through dynamic calculation and real-time adjustment based on hourly simulation cycles, precise optimization configuration of the new energy hydrogen production system can be achieved. The energy allocation is continuously adjusted and optimized according to the system's dynamic operating parameters to achieve globally optimal configuration, ensuring the stability and efficiency of hydrogen supply. Furthermore, considering the influence of wind power data, photovoltaic power data, and initial system configuration parameters, a comprehensive optimization scheme based on multiple inputs is provided. This method is more comprehensive than traditional single-variable optimization methods and can handle more complex dynamic changes.

[0077] Furthermore, this research aims to overcome the limitations of existing methods by developing an optimization configuration algorithm based on multi-threaded parallel computing. This algorithm will fully utilize the computing resources of multi-core CPUs, improving computational efficiency and real-time performance to adapt to the large-scale, dynamically changing configuration requirements of new energy hydrogen production systems. By constructing a comprehensive energy conversion and supply network model and combining it with a multi-threaded concurrent computing algorithm framework, this research not only provides a more accurate system configuration scheme but also enhances the system's flexibility and adaptability while ensuring its economic viability, offering a novel solution for the optimized configuration of new energy hydrogen production systems.

[0078] The various technical features disclosed in the above-described invention, the various technical features disclosed in the following embodiments and examples, and the various technical features disclosed in the accompanying drawings can be freely combined to form various new technical solutions (all of which should be considered as having been recorded in this specification), unless such a combination of technical features is technically infeasible. For example, in one example, feature A+B+C is disclosed, and in another example, feature A+B+D+E is disclosed. Features C and D are equivalent technical means that serve the same function, and technically only one needs to be used; it is impossible to use both simultaneously. Feature E can be technically combined with feature C. Therefore, the solution A+B+C+D should not be considered as having been recorded because it is technically infeasible, while the solution A+B+C+E should be considered as having been recorded. Attached Figure Description

[0079] Figure 1 This is a schematic diagram of a new energy hydrogen production system according to one embodiment of this application.

[0080] Figure 2 This is a schematic diagram of a new energy hydrogen production system optimization configuration method according to one embodiment of this application.

[0081] Figure 3 This is a schematic diagram of the annual hydrogen supply system operation mode of a new energy hydrogen production system optimization configuration method according to one embodiment of this application.

[0082] Figure 4 This is a schematic diagram of a pruning strategy for an optimized configuration method of a new energy hydrogen production system according to one embodiment of this application.

[0083] Figure 5 This is a schematic diagram of the multi-threaded parallel computing underlying architecture of a new energy hydrogen production system optimization configuration method according to one embodiment of this application. Detailed Implementation

[0084] In the following description, many technical details are presented to help the reader better understand this application. However, those skilled in the art will understand that the technical solutions claimed in this application can be implemented even without these technical details and various variations and modifications based on the following embodiments.

[0085] First, a brief overview of some of the innovative aspects of the implementation methods in this application will be provided:

[0086] This application constructs a comprehensive system architecture and provides a clear mathematical model of the state of each component in the new energy hydrogen production system at every moment. This innovation enables the system to accurately predict and adjust its operating state at any given time, providing a solid foundation for achieving efficient system operation and optimized configuration.

[0087] This application proposes an annual operation simulation algorithm and innovatively introduces intraday and interday hydrogen supply smoothing mechanisms. This mechanism can effectively solve the problem of hydrogen supply fluctuations caused by wind and solar resource fluctuations, ensuring the continuity and stability of hydrogen energy supply.

[0088] This application develops an optimization algorithm for system configuration and significantly accelerates the optimization process through a pruning strategy. The algorithm aims to find the system configuration scheme that brings the greatest economic benefits while satisfying system operating constraints.

[0089] This application develops a parallel computing algorithm framework and innovatively introduces concurrency strategies and locking mechanisms, effectively solving the concurrency conflict problem in a multi-threaded environment. By implementing a load balancing mechanism for multi-threaded polling computation, this invention significantly improves computational efficiency and provides strong technical support for the optimized configuration of new energy hydrogen production systems.

[0090] This application significantly improves the accuracy and reliability of system operation by providing a comprehensive system architecture and precise mathematical model to describe the state of each component in the new energy hydrogen production system at every moment. This accurate predictive capability enables the system to respond promptly to changes in external conditions, such as fluctuations in wind and solar resources, thereby ensuring the continuity and stability of hydrogen supply. Furthermore, the intraday and interday hydrogen supply smoothing mechanism introduced in this application effectively solves the problem of hydrogen supply fluctuations, providing a more stable hydrogen supply for downstream applications and reducing the risks and costs caused by supply interruptions or fluctuations. By developing an optimization algorithm for system configuration and employing a pruning strategy to accelerate the optimization process, this application can quickly find the most cost-effective operating mode, maximizing economic benefits and enhancing the competitiveness of the entire new energy hydrogen production industry. Simultaneously, the parallel computing algorithm framework and concurrency control mechanism of this application significantly improve computational efficiency, enabling the handling of larger-scale problems or obtaining solutions in a shorter time, which is particularly important for industries requiring rapid response to market changes. The technological breakthroughs of this application also enhance the system's flexibility and adaptability, enabling it to adapt to different operating conditions and demand changes, ensuring continuous system operation and maximizing its capacity. These advantages work together to provide an innovative solution for this application, which helps to achieve a more sustainable and cost-effective energy future, while also promoting technological progress and industry development in the field of new energy hydrogen production.

[0091] A hydrogen production system consists of several key components, including a wind farm, photovoltaic panels, a hydrogen production system, a hydrogen storage system, an energy storage system, and a hydrogen supply system. These components together form a complex energy conversion and supply network. Before determining the calculation model, this application first meticulously analyzes and divides these components to clarify their operation and mutual influence.

[0092] like Figure 1 As shown, the operation mode of a hydrogen production system can be summarized as follows: wind farms convert wind energy into electrical energy through wind turbines, while photovoltaic panels utilize solar energy to convert light energy into electrical energy. This electrical energy serves as the system's power input, providing power for the entire hydrogen production system.

[0093] After receiving electricity, the system first delivers the power to the hydrogen production unit to generate hydrogen. The generated hydrogen is then transported to downstream hydrogen supply systems to meet the needs of different application scenarios. When the power supply is sufficient and the hydrogen storage system has storage capacity, the system will consider three options in sequence: first, recharge the hydrogen storage system; second, charge the energy storage system; and finally, feed the remaining electricity into the grid. This sequence ensures efficient energy utilization and system flexibility.

[0094] However, if the hydrogen storage system is full, the system will adjust its strategy, prioritizing charging the energy storage system and feeding surplus electricity into the grid. In cases of insufficient power supply and hydrogen production falling short of downstream demand, the system will employ two remedial measures: first, releasing hydrogen from the storage system; second, utilizing the energy storage system to generate and supply hydrogen. These two approaches aim to ensure a continuous hydrogen supply even when power supply is unstable.

[0095] Based on the above system operation logic, the following key factors affecting hydrogen supply can be identified: wind power scale (wind_scale), photovoltaic scale (pv_scale), energy storage system capacity (select_energy_storage), initial energy storage system capacity (start_energy_storage), hydrogen storage system capacity (select_hydro_storage), initial hydrogen storage system capacity (start_hydro_storage), and hydrogen production scale (prod_H). max The scale of wind and solar power directly affects the system's power input, and thus its hydrogen production capacity. The capacity and initial power of the energy storage system determine the system's storage capacity during periods of power surplus, significantly impacting its flexibility and stability. Similarly, the capacity and initial hydrogen quantity of the hydrogen storage system determine the system's storage capacity during periods of hydrogen surplus, also significantly affecting its flexibility and stability. The scale of the hydrogen production system is the core factor influencing hydrogen supply capacity.

[0096] Based on the above analysis of the system, in order to make the objectives, technical solutions and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0097] This application relates to an optimized configuration method for a new energy hydrogen production system, the process of which is as follows: Figure 2 As shown, the method includes the following steps:

[0098] S1. Obtain reference wind power data, photovoltaic power data, and initial system configuration parameters for the target area.

[0099] S2. Based on power data and initial configuration parameters, the operating status of the hydrogen supply system is simulated hourly within a cycle, and the hourly simulation results are dynamically calculated. The hourly simulation results include dynamic operating parameters of multiple new energy hydrogen production systems. The operating status of the new energy hydrogen production system at the next moment will be simulated using the dynamic operating parameters of the new energy hydrogen production system at the previous moment as the original input.

[0100] S3. Dynamically adjust and optimize the energy destination of the new energy hydrogen production system based on the hourly simulation results.

[0101] In an optional embodiment, the initial configuration parameters include: wind power scale, photovoltaic scale, energy storage system capacity, initial energy storage system capacity, hydrogen storage system capacity, initial hydrogen quantity of hydrogen storage system, and hydrogen production scale; at any given time (time i), the real-time power of photovoltaic and wind power is determined by the reference power of the standard year input by the user and the photovoltaic and wind power configuration scale set by the system.

[0102] Therefore, obtaining time-by-time simulation results through dynamic calculation can further include:

[0103] S21. Based on the user-input standard annual reference power, photovoltaic scale, and wind power scale, obtain the actual photovoltaic power and actual wind power at time i:

[0104]

[0105] Among them, pv i Let ref_pv be the actual photovoltaic power at time i. i Let ref_pv_scale be the reference photovoltaic power at time i, ref_pv_scale be the reference photovoltaic scale, select_pv_scale be the selected photovoltaic scale, and wind be the reference photovoltaic power at time i. i Let ref_wind be the actual wind power at time i. i Ref_wind_scale represents the reference wind power at time i, ref_wind_scale represents the reference wind power scale, and select_wind_scale represents the selected wind power scale.

[0106] In an optional embodiment, step S2 may further include:

[0107] S22. Based on the obtained real-time photovoltaic power and wind power, estimate the potential output of combined wind and solar hydrogen production at time i, where:

[0108] S221. If the hydrogen storage tank had reached its maximum capacity at the previous moment. The combined wind and solar hydrogen production at time i is calculated as follows:

[0109]

[0110] Where unit_power is the unit power consumption of the hydrogen production system, and target_H i The optimized target hydrogen supply at time i is limited by the rated hydrogen supply of downstream demand. The hydrogen production at time i is determined by the smaller of two factors: the rated hydrogen supply of downstream demand and the hydrogen production that may be generated when all photovoltaic and wind power resources are used for hydrogen production.

[0111] S222. If the hydrogen storage tank did not reach its maximum capacity at the previous moment. but

[0112] S2221. First calculate the maximum hydrogen capacity that can be stored in the hydrogen storage tank at time i.

[0113]

[0114] Where select_storage_H is the selected system hydrogen storage tank capacity. This represents the remaining hydrogen storage capacity in the hydrogen storage tank at the previous moment.

[0115] S2222. Then calculate the combined wind and solar hydrogen production at time i.

[0116]

[0117] Among them, prod_H max The selected maximum hydrogen production scale;

[0118] The combined wind and solar hydrogen production at time i is influenced by three key factors: the potential hydrogen production if all wind and solar resources are used for hydrogen production, the pre-selected hydrogen production scale (i.e., the maximum hydrogen production capacity of the system per unit time), and the remaining storage capacity of the hydrogen storage tank. In this scenario, the hydrogen production output will be determined by the minimum of these three factors, ensuring that the output of the hydrogen production system neither exceeds the storage capacity of the hydrogen storage tank nor the system's hydrogen production capacity, while fully utilizing the power generation potential of renewable energy.

[0119] In an optional embodiment, step S2 may further include:

[0120] S23. Based on the obtained combined wind and solar hydrogen production at time i, calculate the hydrogen supply gap at time i:

[0121] short_H i =max(target_H i-prod_H i ,0);

[0122] S24. And the remaining electricity after the combined wind and solar hydrogen production at time i:

[0123] left_P i =pv i +wind i -prod_H i *unit_power

[0124] S25. If the remaining battery power is greater than 0, i.e., left_P i If the value is greater than 0, the remaining electricity is used to charge the energy storage system. At time i, the amount of electricity used for charging the energy storage system is determined by two main factors: the remaining electricity of the wind and solar power generation system and the remaining storage capacity of the energy storage system.

[0125] First, we need to calculate the remaining storage capacity of the energy storage system.

[0126] Therefore, in an optional embodiment, step S2 may further include:

[0127] S26. Calculate the remaining storage capacity of the energy storage system:

[0128]

[0129] Among them, avail_E i Let be the maximum amount of energy that the energy storage system can be charged at time i. For the selected system energy storage capacity, The remaining power of the energy storage device at the previous moment;

[0130] S27. Based on the remaining storage capacity of the energy storage system, calculate the actual charging amount of the energy storage system at time i. This charging amount is determined by two factors: the remaining electricity of the wind and solar power generation system and the remaining storage capacity of the energy storage system. Importantly, we must consider the efficiency of the energy storage system during the charging process to accurately calculate the actual storable electricity.

[0131] charge_E i =min(left_P) i *η charg ,avail_E i )

[0132] Where, η charg Improve the charging efficiency of energy storage systems;

[0133] S28. If there is remaining electricity after the energy storage system has finished charging, then the remaining electricity is considered to be fed into the grid. The amount of remaining electricity fed into the grid at time i is:

[0134]

[0135] In an optional embodiment, step S2 may further include:

[0136] S29. If there is a hydrogen supply gap at time i, short_H i >0 and the remaining hydrogen storage capacity in the hydrogen storage tank is lower than the set value left_H i-1 <select_H min If the new energy hydrogen production system starts energy storage discharge to produce hydrogen, the amount of electricity produced by energy storage discharge will be determined by the smaller of the remaining electricity in the energy storage system and the electricity required to fill the hydrogen supply gap.

[0137]

[0138] Where, η output For the discharge efficiency of the energy storage system;

[0139] S210. Based on the amount of hydrogen produced by energy storage discharge, calculate the remaining energy of the energy storage system after time i:

[0140]

[0141] S211. The actual hydrogen charge in the hydrogen storage tank at time i is obtained by adding the electricity generated from hydrogen production through energy storage discharge to the combined wind and solar power hydrogen production:

[0142]

[0143] S212. Based on the optimized target hydrogen supply, the actual hydrogen filling amount of the hydrogen storage tank, and the remaining hydrogen storage amount of the hydrogen storage tank at the previous moment, the actual hydrogen supply at moment i is obtained:

[0144]

[0145] S213. Based on the actual hydrogen supply, obtain the remaining hydrogen in the hydrogen storage tank after time i:

[0146] left_H i =left_H i-1 +input_H i -output_H i .

[0147] In an optional embodiment, step S3 may further include:

[0148] S4. The hourly simulation results are smoothed by adjusting the target hydrogen supply rate iteratively to reduce supply spikes and ensure continuous supply, generating a smoothed hydrogen demand curve, including:

[0149] S41. Determine the minimum hydrogen supply required for stable system operation;

[0150] S42. Set the target hydrogen supply starting from the rated hydrogen supply and adjust it according to system requirements;

[0151] S43. At each moment, calculate the minimum hydrogen supply based on the system operating status;

[0152] S44. If the minimum hourly hydrogen supply is lower than the set minimum hydrogen supply, the hydrogen supply will be adjusted by decreasing the target hydrogen supply by 5%.

[0153] S45. Repeat the previous step until the minimum hydrogen supply per hour thereafter is no longer lower than the set minimum hydrogen supply, or the target hydrogen supply has been reduced to the minimum hydrogen supply.

[0154] Specifically, when conducting an in-depth analysis of the hourly simulation of the intraday hydrogen supply system, we can understand its working principle from the perspective of mathematical models. First, the system's configuration parameters are the macroscopic inputs in the simulation process. These parameters include the scale of photovoltaic power generation, wind power generation, hydrogen production, the capacity of hydrogen storage tanks, and the capacity of the energy storage system. These parameters collectively determine the system's energy output and supply capacity at different time periods.

[0155] Once these configuration parameters are set, the simulation algorithm can automatically perform iterative calculations. This hourly simulation method, by obtaining real-time wind and solar power data for a standard year (8760 hours), enables real-time simulation of the system's dynamic behavior, including fluctuations in energy output, the charging and discharging process of the energy storage system, and the storage and release of hydrogen. The system's operating state at the next moment will be simulated using the remaining hydrogen and energy storage levels from the previous moment as raw inputs (e.g., ...). Figure 3 (As shown).

[0156] Furthermore, in industrial production practice, a key consideration is determining the basic configuration scale of the system to meet the specific downstream demand for hydrogen supply. In this context, traditional production simulation software algorithms are insufficient to handle complex requirements, while manually inputting configuration parameters for testing is not only extremely labor-intensive but also beyond the capabilities of professionals. To address this challenge, this application transforms the production simulation problem into a system optimization problem. The primary issue to be resolved is determining the optimization objective. In the algorithm developed in this application, the main objective of the optimization problem is set as minimizing the total investment. That is, given a fixed unit investment in the five basic configuration factors—photovoltaic scale, wind power scale, hydrogen production scale, energy storage capacity, and hydrogen storage capacity—how to allocate the proportions and scales of these five variables to meet downstream hydrogen supply requirements.

[0157] Therefore, in an optional embodiment, step S3 may further include:

[0158] S51. The hourly simulation results are used to determine the optimization objective, which is to minimize resource consumption by allocating the proportions and scales of five configuration factors, namely photovoltaic scale, wind power scale, hydrogen production scale, energy storage capacity and hydrogen storage capacity, to meet the downstream hydrogen supply demand.

[0159] S52. Introduce feasible solution discrimination conditions, including annual hydrogen supply utilization hours T_supply, the proportion of time when the annual operating hours are not less than the minimum hydrogen supply T_(supply_min), and the percentage of surplus electricity grid connection rate R_grid, to ensure that the optimized configuration scheme meets the hydrogen supply demand while reducing resource waste caused by excessive power allocation.

[0160] The mathematical model for the entire optimization problem is as follows:

[0161]

[0162] object = min factor cost T ·factor

[0163]

[0164] In the formula:

[0165] C wind Unit investment for wind power systems (RMB / kW);

[0166] C pv Unit investment for photovoltaic systems (RMB / kW);

[0167] Unit investment for hydrogen production system (RMB / (Nm3 / h));

[0168] Unit investment for hydrogen storage systems (RMB / kW);

[0169] Unit investment for energy storage systems (RMB / Nm3);

[0170] In this study, given that the optimization model requires cyclic simulation over an 8760-hour period, and that the current state of each variable in the model is closely related to its state at the previous moment, traditional planning algorithms are not suitable for directly solving this model. Therefore, this application adopts an iterative method to achieve global optimization. To further improve the efficiency of the iteration, such as... Figure 4As shown, this application introduces a pruning strategy to accelerate the iteration process. Having identified optimal system configuration parameters, the pruning strategy eliminates solutions with potentially lower resource consumption, effectively saving production simulation computation time and significantly improving the efficiency of system optimization. This is described below:

[0171] In an optional embodiment, step S3 may further include:

[0172] S61. When simulating the operation of the hydrogen supply system in an hourly cycle, the iterative method is used for global optimization, and a pruning strategy is introduced to accelerate the iteration process. Before performing production simulation calculations on a specific combination of system configuration parameters, the resource consumption of the configuration scheme is first evaluated to see if it is lower than the resource consumption of the currently known optimal configuration scheme.

[0173] S62. If the resource consumption of the configuration scheme is not less than the resource consumption of the known optimal scheme, then the scheme is pruned directly, the production simulation process is skipped, and the next configuration scheme is calculated instead.

[0174] This application employs a purely iterative computation method to find the globally optimal configuration. Since the production simulation for each scheme involves 8760 hours of cyclical calculations, and the hydrogen supply smoothing process requires repeated simulations and corrections of past system schemes, the average production simulation runtime for a single scheme is approximately 3 seconds. This significantly extends the software runtime for global configuration optimization.

[0175] Although a pruning algorithm is introduced to improve efficiency, its effectiveness is greatly limited by the optimization range of the user input. Specifically, if the initial parameters set by the user are too low, the optimization algorithm will continuously execute the production simulation process. However, since the optimal solution generated by the production simulation cannot meet the criteria for a feasible solution, the algorithm cannot find the optimal parameters to update, causing the pruning algorithm to fail. In this case, the system's runtime is close to that of a single-threaded algorithm iteratively solving each solution, which will cause great inconvenience in actual production. Therefore, a multi-threaded parallel computing algorithm is needed to improve the efficiency of global configuration optimization, as described below:

[0176] In an optional embodiment, step S3 may further include:

[0177] S71. Based on the pruning strategy, a multi-threaded parallel computing algorithm is introduced. By setting shared memory areas and global parameters, each CPU core of the computer is allowed to start a thread to independently run a production simulation task with a configuration scheme. The load balancing strategy avoids CPU resource idleness, thereby improving computing efficiency.

[0178] S72. During the parallel computing process, a concurrency control strategy is adopted to ensure data consistency and thread safety. By setting different types of locks and performing feasible solution discrimination multiple times, it is ensured that the shared parameters being modified are the current globally optimal parameters, avoiding concurrency conflicts.

[0179] The underlying architecture is as Figure 5 shown. Multiple computing threads can store and access common parameters through a shared memory area. These shared parameters (such as optimizing the scale of wind power, photovoltaic, and hydrogen production, etc.) are used by multiple threads and processes to ensure data consistency and real-time updates. The shared parameters are the key configuration parameters that need to be shared in multi-threaded computing, covering optimization objectives (such as optimizing wind power, photovoltaic, energy storage, etc.) and other system control variables. Each thread will execute computing tasks based on these shared parameters. Multiple CPU cores (CPU_1, CPU_2,..., CPU_n) run in parallel, respectively processing different computing tasks (such as Cal_One_Year, Cal_Invest, etc.). The computing units (CPU_1, CPU_2, etc.) in each process pool independently execute the tasks assigned to them. Different computing tasks can be executed asynchronously, and there is no mandatory sequential dependency between computing tasks, which can effectively improve computing efficiency.

[0180] The algorithm logic is as follows:

[0181] Each thread performs the following operations in sequence:

[0182] 1. Obtain a system configuration scheme from the shared memory area.

[0183] 2. Apply for read access to the shared parameter of the optimal resource consumption amount (denoted as Opti_Cost).

[0184] 3. Judge the size of the resource consumption amount of the current scheme (denoted as Curr_Cost) and Opti_Cost:

[0185] i. Curr_Cost > Opti_Cost: Prune, return to step 1 to process the next scheme;

[0186] ii. Curr_Cost < Opti_Cost: Continue the algorithm.

[0187] 4. Release the read access to Opti_Cost.

[0188] 5. Perform production simulation calculation (time-consuming operation).

[0189] 6. Re-apply for read access to Opti_Cost.

[0190] 7. Run feasible solution discrimination:

[0191] i. If the solution is not feasible: Return to step 1 and continue processing the next solution.

[0192] ii. Otherwise:

[0193] - Request write access to the optimal system configuration parameters and apply an exclusive lock;

[0194] - Update Opti_Cost and related optimal system configuration parameters;

[0195] -Release the lock.

[0196] 8. Clear the cache.

[0197] 9. Return to step 1 to continue processing the next solution.

[0198] Accordingly, embodiments of this application also provide a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the various method embodiments of this application. Computer-readable storage media include permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device. As defined herein, computer-readable storage media does not include transient media, such as modulated data signals and carrier waves.

[0199] Furthermore, embodiments of this application also provide a computer program product, including computer-executable instructions that, when executed by a processor, implement the steps in the above-described method embodiments.

[0200] It should be noted that in this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, 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. Without further limitations, 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. In this application, if it refers to performing an action according to an element, it means performing the action at least according to that element, including two cases: performing the action only according to that element, and performing the action according to that element and other elements. Expressions such as "multiple," "repeatedly," and "various" include two, two times, two kinds, and more than two, more than two times, and more than two kinds.

[0201] The numbering used in describing the steps of a method does not inherently limit the order of these steps. For example, a step with a higher number does not necessarily have to be executed after a step with a lower number; it can be executed first and then second, or even in parallel, as long as this execution order is reasonable to someone skilled in the art. Similarly, multiple steps with consecutively numbered sequences (e.g., step 101, step 102, step 103, etc.) do not restrict other steps from being executed between them; for example, there can be other steps between step 101 and step 102.

[0202] This specification includes combinations of various embodiments described herein. Individual references to embodiments are made (e.g., "one embodiment," "some embodiments," or "preferred embodiments"); however, these embodiments are not mutually exclusive unless indicated to be mutually exclusive or are readily apparent to those skilled in the art. It should be noted that the word "or" is used in a non-exclusive sense throughout this specification unless the context explicitly indicates or requires it.

[0203] All references to this specification are considered to be incorporated integrally into the disclosure of this application so that they can serve as the basis for modifications if necessary. Furthermore, it should be understood that the above descriptions are merely preferred embodiments of this specification and are not intended to limit the scope of protection of this specification. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments of this specification should be included within the scope of protection of one or more embodiments of this specification.

Claims

1. A method for optimizing the configuration of a new energy hydrogen production system, characterized in that, include: Acquire reference wind power data, photovoltaic power data, and initial system configuration parameters for the target area; Based on the power data and initial configuration parameters, the operating status of the hydrogen supply system is simulated hourly within the cycle, and the hourly simulation results are dynamically calculated. The hourly simulation results include multiple dynamic operating parameters of the new energy hydrogen production system. The operating status of the new energy hydrogen production system at the next moment will be simulated using the dynamic operating parameters of the new energy hydrogen production system at the previous moment as the original input. The energy destination of the new energy hydrogen production system is dynamically adjusted and optimized based on the hourly simulation results. The initial configuration parameters include: wind power scale, photovoltaic scale, energy storage system capacity, initial energy storage system capacity, hydrogen storage system capacity, initial hydrogen storage system capacity, and hydrogen production scale. The dynamic calculation to obtain the hourly simulation results further includes: Based on the user-input standard annual reference power, the photovoltaic scale, and the wind power scale, obtain the actual photovoltaic power and actual wind power at time i: in, Let be the actual photovoltaic power at time i. Let be the photovoltaic reference power at time i. For reference of photovoltaic scale, To select the photovoltaic scale, Let i be the actual wind power output at time i. Let i be the wind power reference power at time i. For reference on wind power scale, To select the scale of wind power; Based on the obtained real-time photovoltaic power and real-time wind power, the potential output of combined wind and solar hydrogen production at time i is estimated, where: If the hydrogen storage tank had reached its maximum capacity at the previous moment... The combined wind and solar hydrogen production at time i is calculated as follows: in, For the unit power consumption of the hydrogen production system, The optimized target hydrogen supply at time i is limited by the rated hydrogen supply of downstream demand. The hydrogen production at time i is determined by the smaller of two factors: the rated hydrogen supply of downstream demand and the hydrogen production that may be generated when all photovoltaic and wind power resources are used for hydrogen production. If the hydrogen storage tank mentioned in the previous moment has not reached its maximum capacity. ,but First, calculate the maximum hydrogen capacity that can be stored in the hydrogen storage tank at time i. in, For the selected system hydrogen storage tank capacity, This represents the remaining hydrogen storage capacity in the hydrogen storage tank at the previous moment. Then calculate the combined wind and solar hydrogen production at time i. in, The selected maximum hydrogen production scale; The combined wind and solar hydrogen production at time i is affected by three key factors: the amount of hydrogen produced when all wind and solar resources are used for hydrogen production, the pre-selected hydrogen production scale, i.e. the maximum hydrogen production capacity of the hydrogen production system per unit time, and the remaining storage capacity of the hydrogen storage tank. Based on the obtained combined wind and solar hydrogen production at time i, calculate the hydrogen supply gap at time i: ； And the remaining electricity generated after the combined wind and solar hydrogen production at time i: If the remaining battery power is greater than 0, that is The remaining electricity is then used to charge the energy storage system; Calculate the remaining storage capacity of the energy storage system: in, Let be the maximum amount of energy that the energy storage system can be charged at time i. For the selected system energy storage capacity, The remaining power of the energy storage device at the previous moment; Based on the remaining storage capacity of the energy storage system, calculate the actual charging amount of the energy storage system at time i: in, Improve the charging efficiency of energy storage systems; If the energy storage system has remaining power after charging, the surplus power is considered for grid connection. The amount of surplus power grid connection at time i is: 。 2. The optimized configuration method for a new energy hydrogen production system as described in claim 1, characterized in that, Further includes: If there is a hydrogen supply shortage at time i Furthermore, the remaining hydrogen storage capacity in the hydrogen storage tank is lower than the set value. If the new energy hydrogen production system starts energy storage discharge to produce hydrogen, the amount of electricity produced by the energy storage discharge will be determined by the smaller of the remaining electricity in the energy storage system and the electricity required to fill the hydrogen supply gap. in, For the discharge efficiency of the energy storage system; Based on the amount of hydrogen produced by the energy storage discharge, calculate the remaining energy of the energy storage system after time i: ； The actual hydrogen charge in the hydrogen storage tank at time i is obtained by adding the electricity generated from the hydrogen production through the energy storage discharge to the combined wind and solar power hydrogen production. Based on the optimized target hydrogen supply, the actual hydrogen filling amount of the hydrogen storage tank, and the remaining hydrogen storage amount of the hydrogen storage tank at the previous moment, the actual hydrogen supply at moment i is obtained: Based on the actual hydrogen supply, the remaining hydrogen in the hydrogen storage tank at time i is obtained as follows: 。 3. The optimized configuration method for a new energy hydrogen production system as described in claim 1, characterized in that, Further includes: The hourly simulation results are smoothed by hydrogen supply processing. The target hydrogen supply is iteratively adjusted to reduce supply spikes and ensure supply continuity, generating a smoothed hydrogen demand curve, which includes: Determine the minimum hydrogen supply required for stable system operation; Set the target hydrogen supply starting from the rated hydrogen supply capacity and adjust it according to system requirements; At any given moment, the minimum hydrogen supply is calculated based on the system's operating status; If the minimum hourly hydrogen supply is lower than the set minimum hydrogen supply, the hydrogen supply will be adjusted by decreasing the target hydrogen supply by 5%. Repeat the previous step until the minimum hydrogen supply per hour thereafter is no longer lower than the set minimum hydrogen supply, or the target hydrogen supply has been reduced to the minimum hydrogen supply.

4. The optimized configuration method for a new energy hydrogen production system as described in claim 1, characterized in that, Further includes: The hourly simulation results are used to determine the optimization objective, which is to minimize resource consumption by allocating the proportions and scales of five configuration factors, namely photovoltaic scale, wind power scale, hydrogen production scale, energy storage capacity and hydrogen storage capacity, to meet downstream hydrogen supply demand. Introduce feasible solution criteria, including annual utilization hours of hydrogen supply. The annual operating hours are not less than the proportion of time with minimum hydrogen supply. Percentage of surplus electricity connected to the grid This is to ensure that the optimized configuration scheme meets the hydrogen supply demand while reducing resource waste caused by excessive power allocation.

5. The optimized configuration method for a new energy hydrogen production system as described in claim 1, characterized in that, Further includes: When calculating the operating status of the hydrogen supply system during the hourly cyclic simulation, an iterative method is used for global optimization, and a pruning strategy is introduced to accelerate the iteration process. Before performing production simulation calculations on a specific combination of system configuration parameters, it is first evaluated whether the resource consumption of the configuration scheme is lower than the resource consumption of the currently known optimal configuration scheme. If the resource consumption of the configuration scheme is not less than that of the known optimal scheme, then the scheme is pruned directly, the production simulation process is skipped, and the next configuration scheme is calculated.

6. The optimized configuration method for a new energy hydrogen production system as described in claim 5, characterized in that, Further includes: Based on the pruning strategy, a multi-threaded parallel computing algorithm is introduced. By setting a shared memory area and global parameters, each CPU core of the computer is allowed to start a thread and run a production simulation task with a different configuration scheme independently. A load balancing strategy is used to avoid CPU resource idleness and improve computing efficiency. During parallel computing, concurrency control strategies are employed to ensure data consistency and thread safety. By setting different lock types and performing multiple feasible solution checks, the modified shared parameters are ensured to be the current globally optimal parameters, thus avoiding concurrency conflicts.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions that, when executed by a processor, implement the steps of the method as described in any one of claims 1 to 6.

8. A computer program product comprising computer-executable instructions, characterized in that, When executed by a processor, the computer-executable instructions implement the steps of the method according to any one of claims 1 to 6.

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