Off-grid hydrogen production system capacity optimization method and device, storage medium and electronic equipment

Abstract

The invention provides an off-grid hydrogen production system capacity optimization method and device, a storage medium and electronic equipment, and relates to the technical field of renewable energy sources. The method comprises the following steps: acquiring configuration parameters and new energy power generation power of the off-grid hydrogen production system; defining system operation constraint conditions based on the configuration parameters and new energy power generation power, and constructing a capacity configuration multi-objective optimization model by taking hydrogen leveling cost minimization, new energy consumption proportion maximization, hydrogen production capacity maximization and electrolytic bath equivalent full load hours maximization of the off-grid hydrogen production system in a preset time period as objective functions; and solving the capacity configuration multi-objective optimization model by adopting a non-dominated sorting genetic algorithm to obtain a Pareto target capacity configuration solution set. By intelligently optimizing the capacity configuration of the off-grid hydrogen production system, maximization of new energy utilization, minimization of hydrogen production cost and overall improvement of system operation efficiency can be synergistically realized on the basis of ensuring stable hydrogen supply of the off-grid hydrogen production system.

Description

Technical Field

[0001] The present disclosure relates to the technical field of renewable energy, and in particular, to a method, device, computer-readable storage medium, and electronic device for optimizing the capacity of an off-grid hydrogen production system. Background Art

[0002] With the continuous improvement of the strategic position of hydrogen energy in the comprehensive utilization of renewable energy, constructing a renewable energy hydrogen production system that operates independently of the power grid has become an important direction for the development of energy autonomy. Especially in remote areas with rich wind and light resources but difficult grid access, realizing energy conversion, storage, and transportation through local hydrogen production not only helps to alleviate the problems of curtailment of wind and solar power, but also has good system coupling basis and development potential.

[0003] However, the actual operation of an off-grid hydrogen production system involves the coordinated configuration and dynamic linkage of multiple types of equipment such as wind power generation, photovoltaic power generation, energy storage systems, electrolytic water hydrogen production units, and hydrogen storage devices. Its operating conditions have significant non-linear, multi-period, and strong coupling characteristics, posing higher requirements for the reasonable configuration and optimal operation of the system capacity. At present, related technologies mostly focus on single-objective optimization and are difficult to balance the performance requirements in multiple aspects such as economy, new energy consumption capacity, hydrogen production efficiency, and equipment load balance. In the face of complex load scenarios or dynamic resource distributions, the operating stability and adaptability of the system are insufficient.

[0004] Therefore, there is an urgent need for a method for configuring the capacity of an off-grid hydrogen production system that can simultaneously consider multiple objectives, so as to improve the utilization rate of new energy and ensure the reliability of hydrogen supply while reducing costs.

[0005] It should be noted that the information disclosed in the above background art section is only used to enhance the understanding of the background of the present disclosure, and thus may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0006] The purpose of the embodiments of the present disclosure is to provide a method, device, storage medium, and electronic device for optimizing the capacity of an off-grid hydrogen production system, so as to at least to some extent solve the problem that it is difficult for the off-grid hydrogen production system in related technologies to balance the performance requirements in multiple aspects such as economy, new energy consumption capacity, hydrogen production efficiency, and equipment load balance.

[0007] According to the first aspect of the embodiments of the present disclosure, a method for optimizing the capacity of an off-grid hydrogen production system is provided, including: Obtaining the configuration parameters and new energy power generation of the off-grid hydrogen production system; Based on the configuration parameters and the new - energy power generation, define the system operation constraint conditions, and construct a multi - objective optimization model for capacity configuration with the objectives of minimizing the hydrogen levelized cost, maximizing the new - energy consumption ratio, maximizing the hydrogen production amount, and maximizing the equivalent full - load hours of the electrolyzer within a preset time period for the off - grid hydrogen - production system; Use the non - dominated sorting genetic algorithm to solve the multi - objective optimization model for capacity configuration, and obtain the Pareto optimal capacity configuration solution set.

[0008] In an exemplary embodiment of the present disclosure, the configuration parameters include a preset basic guaranteed load, the charge - discharge efficiency and the discharge - depth - limited power of the energy storage system, the theoretical power consumption per unit of hydrogen of the electrolyzer and the minimum operating power of the electrolyzer, and the effective hydrogen storage volume of the hydrogen storage tank; Based on the configuration parameters and the new - energy power generation, define the system operation constraint conditions, including: According to the preset basic guaranteed load and the new - energy power generation, determine the power supply of the new - energy guaranteed load, and determine the power shortage of the basic guaranteed load according to the power supply of the new - energy guaranteed load and the preset basic guaranteed load; Based on the discharge efficiency and the discharge - depth - limited power of the energy storage system, combined with the power shortage of the basic guaranteed load, determine the power supply of the energy storage system for the guaranteed load; Based on the charge efficiency and the discharge - depth - limited power of the energy storage system and the minimum operating power of the electrolyzer, determine the energy storage charging power, and use the energy storage charging power to determine the energy storage power curtailment; Based on the theoretical power consumption per unit of hydrogen of the electrolyzer, determine the electrolyzer hydrogen - production power, and combined with the effective hydrogen storage volume of the hydrogen storage tank, determine the hydrogen storage tank power curtailment; Calculate the energy storage discharge power, and determine the energy storage charge - discharge loss based on the charge - discharge efficiency of the energy storage system, the energy storage discharge power, the energy storage charging power, and the power supply of the energy storage system for the guaranteed load; Combined with the new - energy power generation, the power supply of the new - energy guaranteed load, the power supply of the energy storage system for the guaranteed load, the energy storage charge - discharge loss, the electrolyzer hydrogen - production power, the energy storage power curtailment, and the hydrogen storage tank power curtailment, define the system operation constraint conditions.

[0009] In an exemplary embodiment of the present disclosure, the system operation constraint conditions include: Supply - demand balance constraint condition: Wherein, is the new - energy power generation, is the power supply of the new - energy guaranteed load, is the power supply of the energy storage system for the guaranteed load, is the energy storage charge - discharge loss, is the hydrogen production power of the electrolyzer, is the power of the energy storage abandoned electricity, is the power of the hydrogen storage tank abandoned electricity; Operating constraints of the energy storage system: Among them, is the energy storage state of the energy storage system at time t, is the depth of discharge limit power, is the rated energy storage capacity of the electrochemistry; Operating constraints of the electrolyzer: Among them, is the hydrogen production power of the electrolyzer, is the minimum operating power of the electrolyzer, is the rated power of the electrolyzer.

[0010] In an exemplary embodiment of the present disclosure, the off-grid hydrogen production system aims to minimize the hydrogen levelized cost, maximize the new energy consumption ratio, maximize the hydrogen production volume, and maximize the equivalent full-load hours of the electrolyzer, including: Among them, is the objective function, is the hydrogen levelized cost, is the new energy consumption ratio, is the annual hydrogen production volume, is the equivalent full-load hours of the electrolyzer; Hydrogen levelized cost is: Among them, is the construction cost of the i th device, is the capacity of the i th device, is the annual hydrogen production volume of the electrolyzer, is the operation and maintenance cost of the i th device, is the internal rate of return of the project, is the total number of years of system operation, is the number of device types, and the device types include electrolyzers, wind farm devices, photovoltaic devices, electrochemical energy storage devices, and hydrogen storage tanks; New energy consumption ratio is: Among them, Power supply for the load guaranteed by new energy Power supply for the load guaranteed by the energy storage system Power for hydrogen production by the electrolyzer Power generation by new energy Is the calculation period corresponding to the preset time period; Annual hydrogen production Is: Wherein, Is the power consumption per standard cubic meter of hydrogen; Equivalent full-load hours of the electrolyzer Is: Wherein, Is the rated power of the electrolyzer.

[0011] In an exemplary embodiment of the present disclosure, obtaining the new energy power generation power of the off-grid hydrogen production system includes: Obtaining the hourly per-unit value power of the wind power array and the hourly per-unit value power of the photovoltaic array within the preset time period; Determine the wind power generation power according to the wind power generation capacity and the hourly per-unit value power of the wind power array, determine the photovoltaic power generation power according to the photovoltaic power generation capacity and the hourly per-unit value power of the photovoltaic array, and determine the new energy power generation power according to the wind power generation power and the photovoltaic power generation power.

[0012] In an exemplary embodiment of the present disclosure, solving the capacity configuration multi-objective optimization model by using the non-dominated sorting genetic algorithm to obtain the Pareto objective capacity configuration solution set includes: Randomly generate initial population individuals according to the capacity configuration variables of the off-grid hydrogen production system; Use the objective function to calculate the fitness values of each of the initial population individuals; According to the fitness values of each of the initial population individuals, perform non-dominated sorting on the initial population individuals, screen out the intermediate population individuals in different non-dominated levels, and obtain the Pareto front solution set based on the crowding distance of the intermediate population individuals; Perform simulated binary crossover operation and polynomial mutation operation on the intermediate population individuals to generate offspring population individuals; Based on the fitness value and crowding distance index, select target individuals from the initial population individuals and the offspring population individuals to form the next generation population; Recalculate the fitness values of the next generation population individuals and perform non-dominated sorting. When the preset iteration number is reached or the Pareto front solution set converges, obtain the Pareto objective capacity configuration solution set.

[0013] In an exemplary embodiment of the present disclosure, the method of performing non-dominated sorting on the initial population individuals according to the fitness values of the initial population individuals, screening out the intermediate population individuals in different non-dominated ranks, and obtaining the Pareto front solution set based on the crowding distance of the intermediate population individuals includes: Dividing the initial population individuals into multiple non-dominated ranks according to the fitness values of the initial population individuals, and screening out the intermediate population individuals in different non-dominated ranks; For each optimization dimension in the objective function, respectively calculate the difference in the objective function values between each of the intermediate population individuals and adjacent individuals; Perform normalization processing on the differences in the objective function values for each optimization dimension, and accumulate the normalized differences in each objective dimension to obtain the crowding distance of the intermediate population individuals; Use the intermediate population individuals whose crowding distance meets the preset crowding condition to form the Pareto front solution set.

[0014] According to a second aspect of the embodiments of the present disclosure, there is provided an off-grid hydrogen production system capacity optimization device, including: A system data acquisition module, configured to acquire the configuration parameters of the off-grid hydrogen production system and the new energy power generation power; An optimization model construction module, configured to define system operation constraint conditions based on the configuration parameters and the new energy power generation power, and construct a capacity configuration multi-objective optimization model with the minimum hydrogen levelized cost, the maximum new energy consumption ratio, the maximum hydrogen production amount, and the maximum equivalent full-load hours of the electrolyzer in a preset time period of the off-grid hydrogen production system as the objective function; An optimization model solving module, configured to solve the capacity configuration multi-objective optimization model by using a non-dominated sorting genetic algorithm to obtain a Pareto objective capacity configuration solution set.

[0015] According to a third aspect of the present disclosure, there is provided a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, any step of the method described in the first aspect is implemented.

[0016] According to a fourth aspect of the present disclosure, there is provided an electronic device, including: A processor and a memory, on which computer-readable instructions are stored, and when the computer-readable instructions are executed by the processor, any step of the method described in the first aspect is implemented.

[0017] The technical solutions provided by the embodiments of the present disclosure may include the following beneficial effects: The off-grid hydrogen production system capacity optimization method provided in the exemplary embodiments of the present disclosure can, by obtaining the configuration parameters of the off-grid hydrogen production system and the new energy power generation, enable the system model to comprehensively describe different capacity combinations, resource output characteristics, and operating boundary conditions during the construction phase, thereby enhancing the adaptability of the model to various configuration scenarios and the integrity of the solution; on this basis, by defining the system operation constraint conditions based on the configuration parameters and the new energy power generation, it is possible to realize the dynamic constraint coupling of multiple operating mechanisms such as wind and light output, electrical load demand, energy storage regulation ability, electrolyzer start-stop logic, and hydrogen storage unit absorption ability, and further support the coordinated optimization configuration of wind power, photovoltaic, energy storage, electrolyzer, and hydrogen storage systems; by constructing a multi-objective optimization model for capacity configuration, it is possible to balance multiple key performance indicators such as hydrogen production cost, renewable energy consumption ratio, load utilization rate of the hydrogen production unit, and annual hydrogen production volume during the capacity decision-making process, realize the balance adjustment between multiple objectives, avoid the problem of the overall operation efficiency of the system decreasing due to focusing on a single objective, and further effectively alleviate the phenomena of abandoned wind and abandoned light caused by the fluctuation of new energy output, improve the utilization level of renewable energy, and ensure the dynamic balance of energy supply and demand during the annual operation cycle; furthermore, by using the non-dominated sorting genetic algorithm for model solution, it is possible to output a Pareto optimal capacity configuration solution set covering multiple performance trade-off paths, providing a flexible and adjustable configuration basis for the system among multiple objectives such as economy, operation reliability, and energy conversion efficiency, and being applicable to the customized deployment requirements under different application scenarios.

[0018] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. Brief Description of the Drawings

[0019] The drawings herein are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present disclosure, and are used together with the specification to explain the principles of the present disclosure. Obviously, the drawings in the following description are only some embodiments of the present disclosure, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0020] Figure 1 The system architecture diagram of an off-grid hydrogen production system capacity optimization method to which the embodiments of the present disclosure can be applied is shown.

[0021] Figure 2 The flowchart of an off-grid hydrogen production system capacity optimization method in the embodiments of the present disclosure is shown.

[0022] Figure 3 The flowchart of defining the system operation constraint conditions in the embodiments of the present disclosure is shown.

[0023] Figure 4The schematic diagram of the principle of an off-grid hydrogen production system in an embodiment of the present disclosure is shown.

[0024] Figure 5 The schematic diagram of a capacity optimization device for an off-grid hydrogen production system in an embodiment of the present disclosure is shown.

[0025] Figure 6 The schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present disclosure is shown.

[0026] In the drawings, the same or corresponding reference numerals indicate the same or corresponding parts. Detailed implementation manners

[0027] The terms used in this specification are only for the purpose of describing specific embodiments and are not intended to limit this specification. The singular forms "a", "the" and "said" used in this specification and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0028] It should be understood that although the terms first, second, third, etc. may be used in this specification to describe various information, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of this specification, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "when" or "while" or "in response to determining".

[0029] Figure 1 The schematic diagram of the system architecture of an off-grid hydrogen production system capacity optimization method to which the embodiments of the present disclosure can be applied is shown.

[0030] As Figure 1 shown, the system architecture 100 may include one or more of terminal devices such as a smart phone 101, a portable computer 102, a desktop computer 103, etc., a network 104, and a server 105. The network 104 is used to provide a medium for a communication link between the terminal device and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or fiber optic cables, etc.

[0031] The terminal device may be various electronic devices having data processing functions, and a display screen is provided on the electronic device, and the display screen is used to display the configuration parameters of the off-grid hydrogen production system, the new energy power generation power, the Pareto target capacity configuration solution set, etc. to the user. The electronic device includes, but is not limited to, the above-mentioned desktop computer, portable computer, smart phone, tablet computer, etc.

[0032] It should be understood that Figure 1 the numbers of the terminal devices, the network, and the server in [[ ]] are merely illustrative. According to the implementation requirements, there can be any number of terminal devices, networks, and servers. For example, the server 105 can be a server cluster composed of multiple servers, etc.

[0033] The off-grid hydrogen production system capacity optimization method provided by the embodiments of the present disclosure can be executed by a terminal device. Correspondingly, the off-grid hydrogen production system capacity optimization device can be disposed in the terminal device. However, it is easy for those skilled in the art to understand that the off-grid hydrogen production system capacity optimization method provided by the embodiments of the present disclosure can also be executed by the server 105. Correspondingly, the off-grid hydrogen production system capacity optimization device can also be disposed in the server 105. No special limitation is made in this exemplary embodiment.

[0034] The embodiments of the present disclosure provide an off-grid hydrogen production system capacity optimization method. Referring to Figure 2 as shown, the method may include steps S210 to S230: Step S210, obtaining the configuration parameters and the new energy power generation of the off-grid hydrogen production system; Step S220, based on the configuration parameters and the new energy power generation, defining the system operation constraint conditions, and taking the minimum hydrogen levelized cost, the maximum new energy consumption ratio, the maximum hydrogen production amount, and the maximum equivalent full-load hours of the electrolyzer of the off-grid hydrogen production system within a preset time period as the objective functions, and constructing a capacity configuration multi-objective optimization model; Step S230, using the non-dominated sorting genetic algorithm to solve the capacity configuration multi-objective optimization model to obtain a Pareto objective capacity configuration solution set.

[0035] By executing the off-grid hydrogen production system capacity optimization method provided by the present disclosure, by obtaining the configuration parameters and the new energy power generation, the model is enabled to have the adaptability to various capacity combinations and operation conditions; based on this, the system operation constraint conditions are constructed, which can realize the dynamic coordination of the wind and light output, the load demand, the energy storage, the electrolyzer, and the hydrogen storage unit, and contribute to the accurate configuration of each device; by constructing a multi-objective optimization model, multiple indicators such as cost, consumption capacity, hydrogen production efficiency, and hydrogen production amount are coordinated, and the overall operation efficiency of the system is improved; by using the non-dominated sorting genetic algorithm to solve, multiple configuration solutions with performance trade-offs can be output to meet the flexible deployment requirements in different scenarios.

[0036] Next, the off-grid hydrogen production system capacity optimization method in this exemplary embodiment will be described in detail.

[0037] In step S210, the configuration parameters and the new energy power generation of the off-grid hydrogen production system are obtained.

[0038] In the exemplary embodiments of the present disclosure, an off-grid hydrogen production system refers to an integrated energy utilization system composed of multiple functional sub-modules such as a wind power generation unit, a photovoltaic power generation unit, an electrochemical energy storage system, an electrolytic cell hydrogen production device, and a hydrogen storage tank. Without relying on external grid access, this system can achieve on-site conversion of new energy and stable supply of hydrogen.

[0039] Correspondingly, the configuration parameters of the off-grid hydrogen production system refer to the key input variables used to characterize the structural scale and operating performance of each sub-module, including but not limited to the preset basic guarantee load, which is used to define the minimum load demand that the system needs to prioritize to meet during each typical time period; the charge-discharge efficiency and depth-of-discharge limit power of the energy storage system, which are used to reflect the energy conversion loss of the electrochemical energy storage unit and its available capacity boundary; the theoretical power consumption per unit of hydrogen of the electrolytic cell and the minimum operating power of the electrolytic cell, which are used to characterize the hydrogen production efficiency and the start-stop control threshold of the electrolytic cell respectively; and the effective hydrogen storage volume of the hydrogen storage tank, which is used to define the matching ability between the hydrogen output and storage of the system.

[0040] The new energy power generation refers to the available power generation generated by the wind power generation unit and the photovoltaic power generation unit under the actual meteorological conditions and corresponding installed capacity constraints during the operation period of the off-grid hydrogen production system, which reflects the energy supply capacity of renewable energy in the system during a specific time period. By building an hourly model of the output of wind and light resources, this power can accurately reflect the intermittent and fluctuating characteristics of the output of renewable energy, and thus provide a dynamic input basis for subsequent energy scheduling and capacity configuration.

[0041] Exemplarily, the hourly per-unit-value power of the wind array and the hourly per-unit-value power of the photovoltaic array within a preset time period, such as 8760 hours throughout the year, can be obtained. The per-unit-value power refers to the normalized output level of the unit installed capacity at each moment, which has characteristics such as being dimensionless, easy to calculate, and applicable across regions, and can be used as the modeling input for standardized resource characteristics. Specifically, resource data such as wind speed and irradiance within the preset time period can be collected, and combined with the power-wind speed characteristic curve of the wind turbine and the performance parameters of the photovoltaic module, the hourly per-unit-value output curves of wind power and photovoltaic power can be constructed, and then the hourly per-unit-value power of the wind array and the hourly per-unit-value power of the photovoltaic array can be obtained.

[0042] Then, the wind power generation is determined according to the wind power generation capacity and the hourly per-unit-value power of the wind array, the photovoltaic power generation is determined according to the photovoltaic power generation capacity and the hourly per-unit-value power of the photovoltaic array, and the new energy power generation is determined according to the wind power generation and the photovoltaic power generation. For example, by hourly superimposing the wind power generation and the photovoltaic power generation, the new energy power generation can be determined.

[0043] For example, the new energy power generation is: Among them, and are the per-hour per-unit power of the wind power array and the per-hour per-unit power of the photovoltaic array respectively, and are the construction scales of the wind power array and the photovoltaic array, that is, the wind power generation capacity and the photovoltaic power generation capacity.

[0044] As one of the core inputs for the modeling and optimization of off-grid hydrogen production systems, new energy power generation can effectively support the construction of system operation constraints, the scheduling of new energy priority power supply, the judgment of the start-stop logic of electrolyzers, and the capacity optimization decision-making process, etc., to ensure a reasonable match between energy supply and demand of the system in the time series dimension.

[0045] In step S220, based on the configuration parameters and new energy power generation, system operation constraint conditions are defined, and a multi-objective optimization model for capacity configuration is constructed with the goal of minimizing the hydrogen levelized cost, maximizing the new energy consumption ratio, maximizing the hydrogen production volume, and maximizing the equivalent full-load hours of the electrolyzer of the off-grid hydrogen production system within a preset time period.

[0046] In some exemplary embodiments, as shown in Figure 3 the process of defining system operation constraint conditions based on configuration parameters and new energy power generation can further include steps S310 to S360: Step S310, according to the preset basic guaranteed load and new energy power generation, determine the power supply of the new energy guaranteed load for the basic load, and determine the power shortage of the basic guaranteed load according to the power supply of the new energy guaranteed load and the preset basic guaranteed load.

[0047] As shown in Figure 4 a schematic diagram of the principle of an off-grid hydrogen production system is shown. In Figure 4 , first, annual new energy resource data 401 is obtained to calculate new energy power generation 402 based on the annual new energy resource data 401. This process is described in detail in step S210 and will not be elaborated here. Then, according to system configuration parameters 403 such as the preset basic guaranteed load, combined with new energy power generation 402, the power supply of the new energy guaranteed load 404 for directly supplying the basic load can be determined.

[0048] In an off-grid hydrogen production system, to ensure the stable operation of key loads, the system operation strategy usually sets the new energy output to be preferentially used to meet the electricity demand of the basic guaranteed load. To better conform to the actual seasonal energy consumption differences, the basic guaranteed load can be set as a fixed proportion of the new energy installed capacity in different seasons. For example, the basic guaranteed load is set to 6% of the installed capacity of wind and light in the heating season and 4% in the non-heating season. Through this strategy, the off-grid hydrogen production system can have a stronger load support ability in cold seasons.

[0049] Specifically, the power supplied by new energy to ensure load is: wherein, is the preset basic guaranteed load, is the power generation of new energy.

[0050] When the output of new energy cannot fully cover the preset basic guaranteed load, the shortage of power of the basic guaranteed load 405 can be further calculated, that is, the difference between the power supplied by new energy to ensure load 404 and the basic guaranteed load. This shortage of power is then adjusted and supplemented by the energy storage system.

[0051] Specifically, the shortage of power of the basic guaranteed load is: If the power generation of new energy is insufficient in consecutive time periods, the total short-term shortage of power of the basic guaranteed load in the consecutive time periods will be calculated, and a charging signal will be sent to the energy storage system to adjust the energy storage system to charge in a timely manner and limit the discharge amount of the energy storage system.

[0052] If the output of new energy is higher than the preset basic guaranteed load, the remaining part can be used as the available power of the hydrogen production system. At this time, the available power of new energy for the hydrogen production system 406 is: wherein, is the available power of new energy. The available power of new energy 406 can be further divided into low-load loss 407 and high-load loss 408, which are respectively denoted as and . The low-load loss 407 refers to the situation where the available power of new energy is lower than the minimum operating power of the electrolyzer, and the electric energy cannot be effectively utilized, resulting in energy waste. The high-load loss 408 refers to the situation where the available power of new energy exceeds the rated power of the electrolyzer, and the excess part cannot be directly supplied to the hydrogen production system, resulting in power overflow loss. Specifically: wherein, is the minimum operating power of the electrolyzer, is the rated power of the electrolyzer.

[0053] When the available power of new energy 406 is between the minimum operating power and the rated power of the electrolyzer, it will be used as the input reference value of the energy storage system. Under the regulation of the energy storage charging signal and the feedback regulation of the hydrogen storage tank, the available power for hydrogen production 409 is obtained through the regulation of the energy storage system, and finally determines the operating state of the electrolyzer.

[0054] Through this step, the basic load priority guarantee strategy based on seasonal regulation is realized, which strengthens the load matching ability and new energy utilization efficiency of the system during the time-series operation process, laying a key foundation for subsequent energy scheduling and constraint modeling.

[0055] Step S320: Based on the discharge efficiency and discharge depth limit power of the energy storage system, and combined with the power shortage of the basic guarantee load, determine the power supply of the energy storage system for the guarantee load.

[0056] In an off-grid hydrogen production system, the energy storage system undertakes a key regulation function, which can balance the volatility of new energy output, optimize the power supply and demand matching, and ensure the stable operation of the hydrogen production system. Due to the uncertainty of wind and solar power generation, the system may face problems of insufficient or excessive new energy power. In order to achieve efficient and stable operation, the control strategy of the energy storage system needs to take into account the load priority and the coordinated optimization of hydrogen energy and electric energy.

[0057] Among them, the primary task of the energy storage system is to give priority to meeting the demand of the basic guarantee load, ensuring that the system can still maintain basic operation in extreme weather or low new energy output. It can be seen from step S310 that when the new energy output cannot meet the preset basic guarantee load, the energy storage system needs to release electric energy for compensation. For example, based on the performance parameters of the energy storage system such as discharge efficiency and discharge depth limit power, the compensation ability for the power supply gap can be evaluated, and the maximum available power that the energy storage system can use for power supply at this moment can be calculated, that is, the power supply of the energy storage system for the guarantee load. Among them, the discharge efficiency represents the effective energy that can be actually provided per unit of electric energy released from the energy storage unit to the load end, which is an indication of energy loss; the discharge depth limit power is the maximum available power that the energy storage device is allowed to release during operation, reflecting the effective capacity boundary of the energy storage system without damaging its service life.

[0058] Specifically, the power supply of the energy storage system for the guarantee load is: Among them, is the power shortage of the basic guarantee load, is the energy storage state of the energy storage system at time t, is the discharge depth limit power, is the energy storage discharge efficiency; Furthermore, there is: Among them, is the energy storage discharge depth, is the electrochemical rated energy storage capacity.

[0059] The energy storage state after the energy storage system supplies power to the guarantee load is: To ensure the demand for basic guaranteed loads in an off-grid hydrogen production system, when the new energy power generation is insufficient for consecutive periods, the total short-term power shortage of the basic guaranteed loads within the consecutive periods will be calculated. , there is: Wherein, n is the total number of consecutive power shortage hours.

[0060] When the energy storage state in a certain period is lower than the total short-term power shortage of the basic guaranteed loads , a charging signal will be given to the energy storage system w = 1, and then adjust through the energy storage system, charge in advance, and appropriately limit the discharge amount to ensure that there is still enough power to supply the basic guaranteed loads in the future for a period of time and avoid power shortage problems in the short term.

[0061] This step not only realizes the load regulation during the new energy fluctuation, but also ensures the safe operation and cycle life of the energy storage system by restricting the depth of discharge of the energy storage and preventing over-discharge, providing key support for the overall energy balance and stable operation of the system.

[0062] Step S330, determine the energy storage charging power based on the charging efficiency of the energy storage system, the depth-of-discharge-limited power, and the minimum operating power of the electrolyzer, and determine the energy storage discarded power using the energy storage charging power.

[0063] When there is still a surplus after the new energy power generation removes the power supply for the basic guaranteed loads, this surplus part can be used to charge the energy storage system or provide energy for the hydrogen production system. However, the charging capacity of the energy storage system is restricted by both the charging efficiency and the depth-of-discharge-limited power. At the same time, to avoid frequent start-stop or conflict with the operation of the hydrogen production unit during the energy storage charging process, it is also necessary to set a charging scheduling threshold in combination with the minimum operating power of the electrolyzer. When the new energy output is higher than the minimum start-stop threshold but fails to meet the operation requirements of the electrolyzer and the energy storage has a charging space, this part of the power is preferentially scheduled to the energy storage system, otherwise it is determined as the remaining electric energy that cannot be utilized, that is, the energy storage discarded power.

[0064] In the exemplary embodiments of the present disclosure, to improve the overall efficiency of the system, the energy storage system needs to combine the feedback regulation of the hydrogen storage system to dynamically optimize the charge-discharge strategy. When the hydrogen storage volume in the hydrogen storage tank approaches the upper limit, continuing hydrogen production may lead to electricity waste. To avoid new energy waste, the energy storage system will absorb the excess electric energy through optimized charging control, reduce the hydrogen production load, and thus alleviate the electricity waste problem. At the same time, to further reduce energy loss, the charging control of the energy storage system also needs to consider the following two types of power mismatch situations: one is that the available power of new energy is lower than the minimum operating power of the electrolyzer, resulting in ineffective utilization of electric energy and thus generating low-load losses; the other is that the available power of new energy exceeds the rated power of the electrolyzer, and the excess part cannot be directly used for hydrogen production, forming high-load losses. For these two situations, the energy storage system can improve the utilization efficiency of electric energy and further optimize the system operation efficiency by reasonably regulating the charging power.

[0065] Among them, the energy storage charging power includes the energy storage target charging power and the energy storage actual charging power. Exemplarily, the energy storage target charging power is: However, it should be noted that the energy storage target charging power is not equal to the energy storage actual charging power, and the scale constraint of the energy storage system also needs to be considered. That is, when formulating the charging strategy, it is necessary to ensure that the target power is within the charge-discharge capacity range of the energy storage system to avoid exceeding the tolerance of the energy storage device and thus ensure the safe and stable operation of the system. Therefore, the energy storage actual charging power is: Among them, is the energy storage charging efficiency.

[0066] Based on this, the energy storage electricity waste power is: By dynamically matching the acceptable power of the energy storage system with the redundant output of the system, the charging scheduling boundary of the energy storage and the power waste caused by insufficient charging are clarified, and the utilization efficiency of the surplus electric energy of new energy is improved.

[0067] Step S340, determine the electrolyzer hydrogen production power based on the theoretical power consumption of the electrolyzer per unit of hydrogen, and combine the effective hydrogen storage volume of the hydrogen storage tank to determine the electricity waste power of the hydrogen storage tank.

[0068] Among them, the theoretical power consumption of the electrolyzer per unit of hydrogen, that is, the electric energy input value required for each standard cubic meter of hydrogen, can establish a one-to-one correspondence between the actual hydrogen production power of the electrolyzer at each moment and the corresponding hydrogen production amount. For example, the electrolyzer hydrogen production power is calculated according to the theoretical power consumption of the electrolyzer per unit of hydrogen and the hydrogen production rate of the electrolyzer at each moment.

[0069] At the same time, in order to avoid system energy waste due to hydrogen production capacity exceeding hydrogen storage capacity, it is necessary to combine the effective hydrogen storage volume of the hydrogen storage tank to determine the remaining hydrogen capacity that can be stored in the current operation cycle. When the hydrogen production rate corresponding to the hydrogen production power of the electrolyzer at the current moment exceeds the instantaneous acceptance capacity or cumulative acceptance limit of the hydrogen storage tank, although the electrolyzer has the operating capacity, the hydrogen produced cannot be effectively stored, and the hydrogen production power needs to be limited. The excess constitutes the abandoned power of the hydrogen storage tank. .

[0070] For example, the effective hydrogen storage volume of the hydrogen storage tank is V(t). The maximum acceptable hydrogen production rate for: The corresponding maximum allowable hydrogen production power is: Finally, although the electrolyzer has a higher operating capacity, its hydrogen production is limited by the capacity of the hydrogen storage tank. The power corresponding to the excess is the abandoned power of the hydrogen storage tank, which is: It can be seen that the power loss of the hydrogen storage tank This reflects the redundant loss caused by the hydrogen storage bottleneck, which causes the electrolyzer operating electricity to be unable to be effectively converted into storable hydrogen.

[0071] It should be noted that in the off-grid hydrogen production system, there may be a dynamic mismatch between the hydrogen production capacity of the electrolyzer and the hydrogen load of the chemical industry, so the system needs to consider the hourly supply and demand balance: for the hydrogen supply side, the hydrogen production of the electrolyzer is calculated through real-time power calculation. For the hydrogen demand side, according to the hourly hydrogen consumption of the downstream chemical industry , calculate the power demand on the system load side, that is, the hourly hydrogen power load of the chemical industry , calculated as follows: in, It is the theoretical power consumption of standard cubic meter of hydrogen, in kWh / Nm³.

[0072] When the hydrogen production of the electrolyzer is greater than the demand for chemical hydrogen, the excess hydrogen will be stored in the hydrogen storage tank. When the hydrogen production cannot meet the demand, the hydrogen storage tank will be replenished to discharge hydrogen to ensure that the load demand is met. At this time, the direct hydrogen supply 410 of the electrolyzer and the hydrogen supply 411 of the hydrogen storage tank together constitute the total hydrogen supply 412 of the system, so that the system has the ability to dynamically respond to fluctuating loads and improve the reliability of hydrogen supply. Among them, the initial hydrogen amount of the hydrogen storage tank at time t is the hydrogen supply 411 of the hydrogen storage tank, and the final hydrogen amount of the hydrogen storage tank at time t-1 can be used for feedback adjustment of the hydrogen storage tank.

[0073] In addition, when the real-time hydrogen storage volume of the hydrogen storage tank reaches the upper limit of its effective hydrogen storage volume, the excess hydrogen cannot be stored in the hydrogen storage tank at this time, resulting in discarded electricity, that is, the discarded electricity power of the hydrogen storage tank is obtained. .

[0074] To avoid the phenomenon of new energy discarded electricity caused by limited hydrogen storage capacity, the system can obtain the current hydrogen storage state of the hydrogen storage tank in real time during operation, including the remaining available volume, and dynamically adjust the hydrogen production power of the electrolyzer based on this information to ensure that hydrogen exceeding the storage upper limit will not be produced, thereby avoiding the waste of electric energy caused by the inability to store hydrogen.

[0075] For example, the hydrogen storage system feeds back hourly , that is, the electricity that can be stored in the energy storage system at the current moment t. When the hydrogen production of the hydrogen storage system is limited (such as the hydrogen storage tank is full), part of the remaining renewable power will be transferred to the energy storage system to avoid discarded electricity. By further affecting the charge and discharge regulation of the energy storage system, the coupling optimization of hydrogen energy and electric energy is realized, and the system stability is improved. Finally, the total hydrogen supply is calculated to meet the downstream application requirements.

[0076] Among them, the hydrogen storage system feeds back hourly is: Among them, is the energy storage state after the energy storage system guarantees the load power supply, is the electrochemical rated energy storage capacity, is the discarded electricity power of the hydrogen storage tank in the previous time period.

[0077] By linking the energy efficiency parameters of the electrolyzer with the hydrogen storage capacity boundary, the dynamic adjustment of the upper and lower limits of the hydrogen production power and the identification of redundant electric energy are realized, which helps to improve the rationality of the electrolyzer operation scheduling and avoid the ineffective energy consumption of the system under the condition of hydrogen storage saturation.

[0078] Step S350, calculate the energy storage discharge power, and determine the energy storage charge and discharge loss based on the charge and discharge efficiency of the energy storage system, the energy storage discharge power, the energy storage charge power, and the energy storage system guarantee load power supply.

[0079] Among them, the energy storage discharge power includes the energy storage target discharge power and the energy storage actual discharge power. When the new energy power generation is unstable and the hydrogen production decreases, the energy storage system will release electric energy to make up for the power gap and ensure the normal operation of the hydrogen production system. At this time, the calculated energy storage target discharge power is: However, the energy storage discharge is also limited. It is necessary to prioritize ensuring the power supply for the base load and comprehensively consider constraints such as the discharge depth of the energy storage system to ensure the safety and service life of the energy storage device, and thus calculate the available discharge power of the energy storage. It is: Among them, is the energy storage discharge efficiency; The actual discharge power of the energy storage It is: The charge and discharge loss of the energy storage It is: Among them, is the actual charging power of the energy storage, is the energy storage charging efficiency, is the actual discharge power of the energy storage, is the power for the energy storage system to ensure the load power supply, is the energy storage discharge efficiency.

[0080] By introducing the coupled calculation of the energy storage system efficiency boundary and the actual dispatching status, the idealized estimation of the energy storage behavior is avoided, and the accuracy of depicting the system energy consumption characteristics is improved.

[0081] Step S360, combining the new energy power generation, the new energy guaranteed load power supply, the energy storage system guaranteed load power supply, the energy storage charge and discharge loss, the electrolyzer hydrogen production power, the energy storage discarded power, and the hydrogen storage tank discarded power, define the system operation constraints.

[0082] In the exemplary embodiment of the present disclosure, the system operation constraints include: Supply-demand balance constraint: Among them, is the new energy power generation, is the new energy guaranteed load power supply, is the energy storage system guaranteed load power supply, is the energy storage charge and discharge loss, is the electrolyzer hydrogen production power, is the energy storage discarded power, is the hydrogen storage tank discarded power. The supply-demand balance constraint can ensure the dynamic matching relationship between wind power, photovoltaic power, energy storage charge and discharge, system load, and hydrogen production power.

[0083] Energy storage system operation constraints: Among them, is the energy storage state of the energy storage system at time t, is the depth of discharge limit power, is the electrochemically rated energy storage capacity. By constraining the charge and discharge states of the energy storage system, the safe and stable operation of the energy storage system can be ensured, and it can be ensured that the service life of the energy storage system will not be affected by over-discharge.

[0084] Operating constraint conditions of the electrolyzer: Among them, is the hydrogen production power of the electrolyzer, is the minimum operating power of the electrolyzer, is the rated power of the electrolyzer. The operating constraint conditions of the electrolyzer can ensure the safe operation of the equipment.

[0085] By defining the system operating constraint conditions, it can be ensured that each set of capacity configuration solutions in the optimization model has complete physical feasibility and operational logic consistency, thereby improving the authenticity of system scheduling simulation and the effectiveness of optimization solution, and supporting the multi-objective evaluation of performance indicators such as hydrogen production, energy utilization rate, and system cost.

[0086] In the exemplary embodiment of the present disclosure, the objective function is to minimize the levelized cost of hydrogen, maximize the new energy consumption ratio, maximize the annual hydrogen production, and maximize the equivalent full-load hours of the electrolyzer for an off-grid hydrogen production system within a preset time period, including: Among them, is the objective function, is the levelized cost of hydrogen, is the new energy consumption ratio, is the annual hydrogen production, is the equivalent full-load hours of the electrolyzer.

[0087] Specifically, the levelized cost of hydrogen is: Among them, is the construction cost of the i th type of equipment, is the capacity of the i th type of equipment, is the annual hydrogen production of the electrolyzer, is the operation and maintenance cost of the i th type of equipment, is the internal rate of return of the project, is the total number of years of system operation, is the number of equipment types, and the equipment types include electrolyzers, wind farm equipment, photovoltaic equipment, electrochemical energy storage equipment, and hydrogen storage tanks; New energy consumption ratio is: Among them, is the power supplied by new energy to ensure the load, is the power supplied by the energy storage system to ensure the load, is the hydrogen production power of the electrolyzer, is the new energy power generation, is the calculation period corresponding to the preset time period; Annual hydrogen production is: Among them, is the theoretical power consumption per standard cubic meter of hydrogen; Equivalent full-load hours of the electrolyzer is: Among them, is the rated power of the electrolyzer. The equivalent full-load hours of the electrolyzer is used to measure the utilization rate of the electrolyzer, ensure its operation stability, and then improve the system economy.

[0088] By maximizing the new energy consumption rate, the full utilization of volatile renewable resources such as wind energy and solar energy can be achieved, significantly reducing the phenomenon of wind and light abandonment, enhancing the system's on-site conversion ability of renewable energy, and thus improving the overall energy utilization efficiency; by minimizing the hydrogen levelized cost, it helps to effectively reduce the production cost per unit of hydrogen on the basis of comprehensively considering factors such as equipment investment, operation and maintenance, and power consumption, and improve the economy and market competitiveness of the system; by maximizing the equivalent full-load hours of the electrolyzer, the operation continuity and load stability of the electrolyzer can be enhanced, reducing the energy consumption fluctuations and equipment losses caused by frequent start-stop, prolonging the service life of the equipment, and improving the operation stability and reliability of the system; by maximizing the annual hydrogen production of the system, the continuous supply capacity for downstream hydrogen-using scenarios such as industry, transportation, or energy storage can be guaranteed, and the output capacity and service guarantee level of the system can be enhanced. Multi-objective collaborative optimization can achieve an all-round balance of economy, energy efficiency, and energy supply capacity, supporting the optimal configuration of the comprehensive performance of the off-grid hydrogen production system under different operating conditions.

[0089] In step S230, the non-dominated sorting genetic algorithm is used to solve the capacity configuration multi-objective optimization model, and the Pareto objective capacity configuration solution set is obtained.

[0090] Among them, the Non-dominated Sorting Genetic Algorithm (NSGA-II) is an efficient multi-objective evolutionary algorithm that can optimize multiple conflicting objectives simultaneously and generate a series of non-dominated solutions on the Pareto front, providing multiple feasible solutions for decision-making.

[0091] Exemplarily, first, according to the capacity configuration variables of the off-grid hydrogen production system, initial population individuals are randomly generated. For example, the capacity configuration variables of the off-grid hydrogen production system include wind power generation capacity, photovoltaic power generation capacity, electrochemical energy storage capacity, electrolyzer hydrogen production capacity, and hydrogen storage tank capacity, and each initial population individual represents a specific capacity configuration scheme.

[0092] Next, the fitness values of each initial population individual are calculated using the objective functions to quantitatively evaluate the performance of the individual solutions based on objectives such as the hydrogen levelized cost, new energy consumption rate, annual hydrogen production, and electrolyzer equivalent full-load hours.

[0093] Based on the fitness values of each initial population individual, non-dominated sorting is performed on the initial population individuals, and the intermediate population individuals in different non-dominated ranks are screened out, and the Pareto front solution set is obtained based on the crowding distance of the intermediate population individuals. For example, the initial population individuals are divided into multiple non-dominated ranks according to the fitness values of each initial population individual under the objective functions. The first rank contains the optimal solutions that are not dominated by any other individuals currently, the second rank contains the sub-optimal solutions that are only dominated by the individuals in the first rank, and so on, completing the ranking of the entire population. The individuals within each rank do not dominate each other and jointly represent the multi-objective trade-off results in the current search space.

[0094] Subsequently, the intermediate population individuals in different non-dominated ranks are screened out from these different ranks as the candidate set for subsequent crowding distance evaluation and Pareto front construction. To further measure the distribution density of individuals in the multi-objective space, each objective function dimension needs to be analyzed. For each optimization dimension in the objective functions, such as the hydrogen levelized cost, new energy consumption rate, etc., the differences in the objective function values between each intermediate population individual and its adjacent individuals are calculated respectively, reflecting the sparsity of the individual in this dimension. Since the numerical dimensions of different objective functions may vary, to avoid bias effects, the differences in the objective function values under each optimization dimension are normalized, that is, they are scaled according to the maximum and minimum value intervals of the objective function in the population. Further, the normalized differences in each objective dimension are accumulated to obtain the crowding distance of the intermediate population individuals. The larger the value, the sparser the position of the individual in the solution space.

[0095] Finally, the intermediate population individuals whose crowding distance meets the preset crowding condition are used. For example, the intermediate population individuals with a crowding distance higher than the average value or the first several maximum values are selected to form the Pareto front solution set. This solution set not only achieves non-dominated optimality in terms of the objective function values, but also ensures the diversity of the solution space distribution, which is beneficial to the stability and global convergence of the solution set in the subsequent iteration process, and improves the representativeness and engineering application value of the final optimization result.

[0096] After the initial population is sorted, the intermediate population individuals are selected as the basis for genetic operations. Specifically, the simulated binary crossover operation and polynomial mutation operation are performed on the intermediate population individuals to generate offspring population individuals, expand the search space and enhance the diversity of the population. Subsequently, the initial population and the offspring population are merged, and based on the fitness value and crowding distance index, the target individuals with higher fitness and uniform distribution are selected from the initial population individuals and the offspring population individuals to form the next generation population.

[0097] The fitness values of the next generation population individuals are recalculated, and non-dominated sorting is performed to update the current Pareto front solution set. This evolutionary process continues iteratively until the preset number of iterations is reached or the Pareto front solution set converges, and then the evolution is terminated to obtain the Pareto target capacity configuration solution set, which is the capacity configuration scheme that achieves the trade-off optimality under multiple objective indicators, providing reliable multi-scheme support for the actual deployment of the off-grid hydrogen production system.

[0098] In this exemplary embodiment, an off-grid hydrogen production system capacity optimization device is also provided. Refer to Figure 5 As shown, the off-grid hydrogen production system capacity optimization device 500 may include a system data acquisition module 510, an optimization model construction module 520, and an optimization model solving module 530, where: The system data acquisition module 510 is configured to acquire the configuration parameters of the off-grid hydrogen production system and the new energy power generation power. The optimization model construction module 520 is configured to define the system operation constraint conditions based on the configuration parameters and the new energy power generation power, and construct a capacity configuration multi-objective optimization model with the minimum hydrogen levelized cost, the maximum new energy consumption ratio, the maximum hydrogen production amount, and the maximum electrolyzer equivalent full load hours of the off-grid hydrogen production system within a preset time period as the objective functions. The optimization model solving module 530 is configured to solve the capacity configuration multi-objective optimization model by using the non-dominated sorting genetic algorithm to obtain the Pareto target capacity configuration solution set.

[0099] The specific details of each module of the above off-grid hydrogen production system capacity optimization device have been described in detail in the corresponding off-grid hydrogen production system capacity optimization method, so they will not be elaborated here.

[0100] Exemplary embodiments of the present disclosure also provide a computer-readable storage medium having a program product stored thereon that can implement the methods described above in this specification. In some possible embodiments, various aspects of the present disclosure may also be implemented in the form of a program product, which includes program code that, when the program product runs on an electronic device, causes the electronic device to execute the steps according to various exemplary embodiments of the present disclosure described in the "Exemplary Methods" section above in this specification.

[0101] The program product may be a portable compact disc read-only memory (CD-ROM) and include the program code, and may run on an electronic device, such as a personal computer. However, the program product of the present disclosure is not limited thereto. In this document, the readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0102] The program product may employ any combination of one or more readable media. The readable media may be a readable signal medium or a readable storage medium. The readable storage medium may, for example, but not be limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0103] The computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries the readable program code. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable signal medium may also be any readable medium other than the readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0104] The program code contained on the readable medium may be transmitted by any appropriate medium, including but not limited to wireless, wired, optical fiber cable, RF, etc., or any suitable combination of the above.

[0105] Program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C#, C++, etc., and also including conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, executed as a stand-alone software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0106] Furthermore, an exemplary embodiment of the present disclosure also provides an electronic device capable of implementing the above off-grid hydrogen production system capacity optimization method.

[0107] The following refers to Figure 6 to describe the electronic device 600 according to such an embodiment of the present disclosure. Figure 6 The illustrated electronic device 600 is merely an example and should not impose any limitation on the functions and usage scope of the embodiments of the present disclosure.

[0108] As Figure 6 shown, the electronic device 600 is presented in the form of a general-purpose computing device. The components of the electronic device 600 may include, but are not limited to: at least one of the above processing units 610, at least one of the above storage units 620, a bus 630 connecting different system components (including the storage unit 620 and the processing unit 610), and a display unit 640.

[0109] The storage unit 620 stores program code that can be executed by the processing unit 610, so that the processing unit 610 executes the steps according to various exemplary embodiments of the present disclosure described in the above "Exemplary Method" section of this specification. For example, the processing unit 610 may execute the method steps in the exemplary embodiments of the present disclosure.

[0110] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 621 and / or a cache storage unit (Cache) 622, and may further include a read-only storage unit (ROM) 623.

[0111] The storage unit 620 may also include a program / utility 624 having a set (at least one) of program modules 625. Such program modules 625 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment.

[0112] The bus 630 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus structures.

[0113] The electronic device 600 may also communicate with one or more external devices 670 (such as a keyboard, a pointing device, a Bluetooth device, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or may communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (such as a router, a modem, etc.). Such communication may be carried out through the input / output (I / O) interface 650. Moreover, the electronic device 600 may also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through the network adapter 660. As shown in the figure, the network adapter 660 communicates with other modules of the electronic device 600 through the bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0114] Through the description of the above embodiments, those skilled in the art can easily understand that the exemplary embodiments described herein can be implemented by software, or can be implemented by a combination of software and necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which may be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0115] In addition, the above drawings are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present disclosure, rather than for limiting purposes. It is easy to understand that the processes shown in the above drawings do not indicate or limit the time sequence of these processes. Additionally, it is also easy to understand that these processes may be executed synchronously or asynchronously in, for example, multiple modules.

[0116] From the description of the above embodiments, those skilled in the art can easily understand that the exemplary embodiments described herein can be implemented by software or by a combination of software and necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (such as a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0117] After considering the specification and practicing the invention disclosed herein, those skilled in the art will readily conceive of other embodiments of the present disclosure. This application is intended to cover any variations, uses, or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common general knowledge or conventional technical means in the technical field not disclosed in the present disclosure. The specification and embodiments are only regarded as exemplary, and the true scope and spirit of the present disclosure are pointed out by the claims.

[0118] It should be understood that the present disclosure is not limited to the exact structures already described and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present disclosure is only limited by the appended claims.

Claims

1. A method for optimizing the capacity of an off-grid hydrogen production system, characterized in that: include: Obtain the configuration parameters of the off-grid hydrogen production system and the power generation capacity of renewable energy; Based on the configuration parameters and the power generation of new energy, the system operation constraints are defined, and the objective function is to minimize the levelized cost of hydrogen, maximize the proportion of new energy consumption, maximize the hydrogen production, and maximize the equivalent full load hours of the electrolyzer within a preset time period of the off-grid hydrogen production system, and to build a capacity configuration multi-objective optimization model; A non-dominated sorting genetic algorithm is used to solve the capacity configuration multi-objective optimization model to obtain a Pareto target capacity configuration solution set.

2. The off-grid hydrogen production system capacity optimization method according to claim 1, characterized in that: The configuration parameters include the preset basic guarantee load, the charging and discharging efficiency of the energy storage system and the discharge depth limit power, the theoretical power consumption per unit hydrogen of the electrolyzer and the minimum working power of the electrolyzer, and the effective hydrogen storage capacity of the hydrogen storage tank; Defining system operation constraints based on the configuration parameters and the new energy generation power includes: Determine the power supply power of the new energy guarantee load according to the preset basic guarantee load and the new energy power generation power, and determine the power shortage of the basic guarantee load according to the power supply power of the new energy guarantee load and the preset basic guarantee load; Based on the discharge efficiency and discharge depth limit power of the energy storage system and the basic guaranteed load power shortage, determine the energy storage system guaranteed load power supply power; Determine the energy storage charging power based on the charging efficiency and discharge depth limit power of the energy storage system and the minimum working power of the electrolyzer, and determine the energy storage abandoned power using the energy storage charging power; The hydrogen production power of the electrolyzer is determined based on the theoretical power consumption per unit of hydrogen in the electrolyzer, and the abandoned power of the hydrogen storage tank is determined in combination with the effective hydrogen storage volume of the hydrogen storage tank; Calculate the energy storage discharge power, and determine the energy storage charge and discharge loss based on the charge and discharge efficiency, energy storage discharge power, energy storage charge power, and energy storage system load power supply power of the energy storage system; The system operation constraints are defined in combination with the new energy power generation power, the new energy load guarantee power supply power, the energy storage system load guarantee power supply power, the energy storage charging and discharging loss, the electrolyzer hydrogen production power, the energy storage power abandonment power and the hydrogen storage tank power abandonment power.

3. The off-grid hydrogen production system capacity optimization method according to claim 2, characterized in that: The system operation constraints include: Supply and demand balance constraints: in, For renewable energy power generation, To ensure the power supply of loads for new energy, To ensure the load power supply for the energy storage system, is the energy storage charging and discharging loss, is the hydrogen production power of the electrolyzer, For energy storage, the abandoned power The abandoned power of the hydrogen storage tank; Energy storage system operation constraints: in, is the energy storage state of the energy storage system at time t, To limit the discharge depth, is the electrochemical rated energy storage capacity; Electrolyzer operation constraints: in, is the hydrogen production power of the electrolyzer, is the minimum operating power of the electrolyzer, is the rated power of the electrolyzer.

4. The off-grid hydrogen production system capacity optimization method according to claim 1, characterized in that: The objective function is to minimize the levelized cost of hydrogen, maximize the proportion of new energy consumption, maximize the hydrogen production, and maximize the equivalent full load hours of the electrolyzer within a preset time period of the off-grid hydrogen production system, including: in, is the objective function, is the levelized cost of hydrogen, is the proportion of new energy consumption, is the annual hydrogen production, is the equivalent full load hours of the electrolyzer; Levelized cost of hydrogen for: in, For the i The construction cost of this equipment, For the i The capacity of the equipment, is the annual hydrogen production of the electrolyzer, For the i The operation and maintenance costs of the equipment is the project internal rate of return, is the total number of years the system has been in operation, is the number of equipment types, including electrolyzers, wind farm equipment, photovoltaic equipment, electrochemical energy storage equipment, and hydrogen storage tanks; New energy consumption ratio for: in, To ensure the power supply of loads for new energy, To ensure the load power supply for the energy storage system, is the hydrogen production power of the electrolyzer, For renewable energy power generation, is the calculation cycle corresponding to the preset time period; Annual hydrogen production for: in, The power consumption per standard cubic meter of hydrogen; Equivalent full load hours of electrolyzer for: in, is the rated power of the electrolyzer.

5. The off-grid hydrogen production system capacity optimization method according to claim 1, characterized in that: Obtain renewable energy power for off-grid hydrogen production systems, including: Obtaining the hourly per-unit power of the wind array and the hourly per-unit power of the photovoltaic array within a preset time period; The wind power generation power is determined according to the wind power generation capacity and the hourly per-unit power of the wind array, the photovoltaic power generation power is determined according to the photovoltaic power generation capacity and the hourly per-unit power of the photovoltaic array, and the new energy power generation power is determined according to the wind power generation power and the photovoltaic power generation power.

6. The off-grid hydrogen production system capacity optimization method according to claim 1, characterized in that: The non-dominated sorting genetic algorithm is used to solve the capacity configuration multi-objective optimization model to obtain a Pareto target capacity configuration solution set, including: Randomly generating initial population individuals according to the capacity configuration variables of the off-grid hydrogen production system; Calculating the fitness value of each individual in the initial population using the objective function; According to the fitness value of each of the initial population individuals, the initial population individuals are non-dominated sorted, the intermediate population individuals at different non-dominated levels are screened out, and the Pareto front solution set is obtained based on the crowding distance of the intermediate population individuals; Using simulated binary crossover operation and polynomial mutation operation on the intermediate population individuals to generate offspring population individuals; Based on the fitness value and the crowding distance index, target individuals are selected from the initial population individuals and the offspring population individuals to form the next generation population; The fitness values ​​of the next generation population individuals are recalculated and non-dominated sorting is performed, and when a preset number of iterations is reached or the Pareto front solution set converges, the Pareto target capacity configuration solution set is obtained.

7. The off-grid hydrogen production system capacity optimization method according to claim 6, characterized in that: According to the fitness value of each of the initial population individuals, the initial population individuals are non-dominated sorted, the intermediate population individuals at different non-dominated levels are screened out, and the Pareto front solution set is obtained based on the crowding distance of the intermediate population individuals, including: According to the fitness value of each of the initial population individuals, the initial population individuals are divided into a plurality of non-dominated levels, and the intermediate population individuals at different non-dominated levels are screened out; For each optimization dimension in the objective function, respectively calculating the objective function value difference between each intermediate population individual and the adjacent individual; Normalizing the objective function value difference under each optimization dimension, and accumulating the normalized objective dimension differences to obtain the crowding distance of the intermediate population individuals; The Pareto front solution set is formed by using the intermediate population individuals whose crowding distances satisfy the preset crowding conditions.

8. A capacity optimization device for an off-grid hydrogen production system, characterized in that: include: System data acquisition module, used to obtain the configuration parameters of the off-grid hydrogen production system and the power generation of new energy; An optimization model building module is used to define system operation constraints based on the configuration parameters and renewable energy power generation, and to build a capacity configuration multi-objective optimization model with the objective function of minimizing the levelized cost of hydrogen, maximizing the proportion of renewable energy consumption, maximizing the hydrogen production, and maximizing the equivalent full load hours of the electrolyzer within a preset time period of the off-grid hydrogen production system; The optimization model solving module is used to solve the capacity configuration multi-objective optimization model by using a non-dominated sorting genetic algorithm to obtain a Pareto target capacity configuration solution set.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: include: Processor; and A memory having computer-readable instructions stored thereon, wherein the computer-readable instructions, when executed by the processor, implement the method according to any one of claims 1 to 7.

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